Gene expression markers for predicting response to chemotherapy

ABSTRACT

The present invention provides sets of genes the expression of which is important in the prognosis of cancer. In particular, the invention provides gene expression information useful for predicting whether cancer patients are likely to have a beneficial treatment response to chemotherapy FHIT; MTA1; ErbB4; FUS; BBC3; IGF1R; CD9; TP53BP1; MUC1; IGFBP5; rhoC; RALBP1; STAT3; ERK1; SGCB; DHPS; MGMT; CRIP2; ErbB3; RAP1GDS1; CCND1; PRKCD; Hepsin; AK055699; ZNF38; SEMA3F; COL1A1; BAG1; AKT1; COL1A2; Wnt.5a; PTPD1; RAB6C; GSTM1, BCL2, ESR1; or the corresponding expression product, is determined, said report includes a prediction that said subject has a decreased likelihood of response to chemotherapy.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention provides sets of genes the expression of which isimportant in the prognosis of cancer. In particular, the inventionprovides gene expression information useful for predicting whethercancer patients are likely to have a beneficial treatment response tochemotherapy.

2. Description of the Related Art

Oncologists have a number of treatment options available to them,including different combinations of chemotherapeutic drugs that arecharacterized as “standard of care,” and, a number of drugs that do notcarry a label claim for particular cancer, but for which there isevidence of efficacy in that cancer. Best likelihood of good treatmentoutcome requires that patients be assigned to optimal available cancertreatment, and that this assignment be made as quickly as possiblefollowing diagnosis. In particular, it is important to determine thelikelihood of patient response to “standard of care” chemotherapybecause chemotherapeutic drugs such as anthracyclines and taxanes havelimited efficacy and are toxic. The identification of patients who aremost or least likely to respond thus could increase the net benefitthese drugs have to offer, and decrease the net morbidity and toxicity,via more intelligent patient selection.

Currently, diagnostic tests used in clinical practice are singleanalyte, and therefore do not capture the potential value of knowingrelationships between dozens of different markers. Moreover, diagnostictests are frequently not quantitative, relying on immunohistochemistry.This method often yields different results in different laboratories, inpart because the reagents are not standardized, and in part because theinterpretations are subjective and cannot be easily quantified.RNA-based tests have not often been used because of the problem of RNAdegradation over time and the fact that it is difficult to obtain freshtissue samples from patients for analysis. Fixed paraffin-embeddedtissue is more readily available and methods have been established todetect RNA in fixed tissue. However, these methods typically do notallow for the study of large numbers of genes (DNA or RNA) from smallamounts of material. Thus, traditionally fixed tissue has been rarelyused other than for immunohistochhemistry detection of proteins.

In the last few years, several groups have published studies concerningthe classification of various cancer types by microarray gene expressionanalysis (see, e.g. Golub et al., Science 286:531-537 (1999);Bhattacharjae et al., Proc. Natl. Acad. Sci. USA 98:13790-13795 (2001);Chen-Hsiang et al., Bioinformatics 17 (Suppl. 1):S316-S322 (2001);Ramaswamy et al., Proc. Natl. Acad. Sci. USA 98:15149-15154 (2001)).Certain classifications of human breast cancers based on gene expressionpatterns have also been reported (Martin et al., Cancer Res.60:2232-2238 (2000); West et al., Proc. Natl. Acad. Sci. USA98:11462-11467 (2001); Sorlie et al., Proc. Natl. Acad. Sci. USA98:10869-10874 (2001); Yan et al., Cancer Res. 61:8375-8380 (2001)).However, these studies mostly focus on improving and refining thealready established classification of various types of cancer, includingbreast cancer, and generally do not provide new insights into therelationships of the differentially expressed genes, and do not link thefindings to treatment strategies in order to improve the clinicaloutcome of cancer therapy.

Although modern molecular biology and biochemistry have revealedhundreds of genes whose activities influence the behavior of tumorcells, state of their differentiation, and their sensitivity orresistance to certain therapeutic drugs, with a few exceptions, thestatus of these genes has not been exploited for the purpose ofroutinely making clinical decisions about drug treatments. One notableexception is the use of estrogen receptor (ER) protein expression inbreast carcinomas to select patients to treatment with anti-estrogendrugs, such as tamoxifen. Another exceptional example is the use ofErbB2 (Her2) protein expression in breast carcinomas to select patientswith the Her2 antagonist drug Herceptin® (Genentech, Inc., South SanFrancisco, Calif.).

Despite recent advances, the challenge of cancer treatment remains totarget specific treatment regimens to pathogenically distinct tumortypes, and ultimately personalize tumor treatment in order to maximizeoutcome. Hence, a need exists for tests that simultaneously providepredictive information about patient responses to the variety oftreatment options. This is particularly true for breast cancer, thebiology of which is poorly understood. It is clear that theclassification of breast cancer into a few subgroups, such as the ErbB2positive subgroup, and subgroups characterized by low to absent geneexpression of the estrogen receptor (ER) and a few additionaltranscriptional factors (Perou et al., Nature 406:747-752 (2000)), doesnot reflect the cellular and molecular heterogeneity of breast cancer,and does not allow the design of treatment strategies maximizing patientresponse. Breast cancer is the most common type of cancer among women inthe United States and is the leading cause of cancer deaths among womenages 40-59. Therefore, there is a particularly great need for aclinically validated breast cancer test predictive of patient responseto chemotherapy.

SUMMARY OF THE INVENTION

The present invention provides gene sets useful in predicting theresponse of cancer, e.g. breast cancer patients to chemotherapy. Inaddition, the invention provides a clinically validated cancer, e.g.breast cancer, test, predictive of patient response to chemotherapy,using multi-gene RNA analysis. The present invention accommodates theuse of archived paraffin-embedded biopsy material for assay of allmarkers in the relevant gene sets, and therefore is compatible with themost widely available type of biopsy material.

In one aspect, the present invention concerns a method for predictingthe response of a subject diagnosed with cancer to chemotherapycomprising determining the expression level of one or more prognosticRNA transcripts or their expression products in a biological samplecomprising cancer cells obtained from said subject, wherein thepredictive RNA transcript is the transcript of one or more genesselected from the group consisting of TBP; ILT.2; ABCC5; CD18; GATA3;DICER1; MSH3; GBP1; IRS1; CD3z; fas1; TUBB; BAD; ERCC1; MCM6; PR; APC;GGPS1; KRT18; ESRRG; E2F1; AKT2; A.Catenin; CEGP1; NPD009; MAPK14;RUNX1; ID2; G.Catenin; FBXO5; FHIT; MTA1; ERBB4; FUS; BBC3; IGF1R; CD9;TP53BP1; MUC1; IGFBP5; rhoC; RALBP1; CDC20; STAT3; ERK1; HLA.DPB1; SGCB;CGA; DHPS; MGMT; CRIP2; MMP12; ErbB3; RAP1GDS1; CDC25B; IL6; CCND1;CYBA; PRKCD; DR4; Hepsin; CRABP1; AK055699; Contig.51037; VCAM1; FYN;GRB7; AKAP.2; RASSF1; MCP1; ZNF38; MCM2; GBP2; SEMA3F; CD31, COL1A1;ER2; BAG1; AKT1; COL1A2; STAT1; Wnt.5a; PTPD1; RAB6C; TK1, ErbB2, CCNB1,BIRC5, STK6, MKI67, MYBL2, MMP11, CTSL2, CD68, GSTM1, BCL2, ESR1 wherein

-   -   (a) for every unit of increased expression of one or more of        ILT.2; CD18; GBP1; CD3z; fas1; MCM6; E2F1; ID2; FBXO5; CDC20;        HLA.DPB1; CGA; MMP12; CDC25B; IL6; CYBA; DR4; CRABP1;        Contig.51037; VCAM1; FYN; GRB7; AKAP.2; RASSF1; MCP1; MCM2;        GBP2; CD31; ER2; STAT1; TK1; ERBB2, CCNB1, BIRC5, STK6, MKI67,        MYBL2, MMP11, CTSL2 and CD68; or the corresponding expression        product, said subject is predicted to have an increased        likelihood of response to chemotherapy; and    -   (b) for every unit of increased expression of one or more of        TBP; ABCC5; GATA3; DICER1; MSH3; IRS1; TUBB; BAD; ERCC1; PR;        APC; GGPS1; KRT18; ESRRG; AKT2; A.Catenin; CEGP1; NPD009;        MAPK14; RUNX1; G.Catenin; FHIT; MTA1; ErbB4; FUS; BBC3; IGF1R;        CD9; TP53BP1; MUC1; IGFBP5; rhoC; RALBP1; STAT3; ERK1; SGCB;        DHPS; MGMT; CRIP2; ErbB3; RAP1GDS1; CCND1; PRKCD; Hepsin;        AK055699; ZNF38; SEMA3F; COL1A1; BAG1; AKT1; COL1A2; Wnt.5a;        PTPD1; RAB6C; GSTM1, BCL2, ESR1; or the corresponding expression        product, said subject is predicted to have a decreased        likelihood of response to chemotherapy.

In a particular embodiment, in the above method the predictive RNAtranscript is the transcript of one or more genes selected from thegroup consisting of TBP; ILT.2; ABCC5; CD18; GATA3; DICER1; MSH3; GBP1;IRS1; CD3z; fas1; TUBB; BAD; ERCC1; MCM6; PR; APC; GGPS1; KRT18; ESRRG;E2F1; AKT2; A.Catenin; CEGP1; NPD009; MAPK14; RUNX1; ID2; G.Catenin;FBXO5; FHIT; MTA1; ERBB4; FUS; BBC3; IGF1R; CD9; TP53BP1; MUC1; IGFBP5;rhoC; RALBP1; CDC20; STAT3; ERK1; HLA.DPB1; SGCB; CGA; DHPS; MGMT;CRIP2; MMP12; ErbB3; RAP1GDS1; CDC25B; IL6; CCND1; CYBA; PRKCD; DR4;Hepsin; CRABP1; AK055699; Contig.51037; VCAM1; FYN; GRB7; AKAP.2;RASSF1; MCP1; ZNF38; MCM2; GBP2; SEMA3F; CD31; COL1A1; ER2; BAG1; AKT1;COL1A2; STAT1; Wnt.5a; PTPD1; RAB6C; and TK1.

In another embodiment, the response is a complete pathological response.

In a preferred embodiment, the subject is a human patient.

The cancer can be any types of cancer but preferably is a solid tumor,such as breast cancer, ovarian cancer, gastric cancer, colon cancer,pancreatic cancer, prostate cancer and lung cancer.

If the tumor is breast cancer, it can, for example, be invasive breastcancer, or stage II or stage III breast cancer.

In a particular embodiment, the chemotherapy is adjuvant chemotherapy.

In another embodiment, the chemotherapy is neoadjuvant chemotherapy.

The neoadjuvant chemotherapy may, for example, comprise theadministration of a taxane derivative, such as docetaxel and/orpaclitaxel, and/or other anti-cancer agents, such as, members of theanthracycline class of anti-cancer agents, doxorubicin, topoisomeraseinhibitors, etc.

The method may involve determination of the expression levels of atleast two, or at least five, or at least ten, or at least 15 of theprognostic transcripts listed above, or their expression products.

The biological sample may be e.g. a tissue sample comprising cancercells, where the tissue can be fixed, paraffin-embedded, or fresh, orfrozen.

In a particular embodiment, the tissue is from fine needle, core, orother types of biopsy.

In another embodiment, the tissue sample is obtained by fine needleaspiration, bronchial lavage, or transbronchial biopsy.

The expression level of said prognostic RNA transcript or transcriptscan be determined, for example, by RT-PCR or an other PCR-based method,immunohistochemistry, proteomics techniques, or any other methods knownin the art, or their combination.

In an embodiment, the assay for the measurement of said prognostic RNAtranscripts or their expression products is provided is provided in theform of a kit or kits.

In another aspect, the invention concerns an array comprisingpolynucleotides hybridizing to a plurality of the following genes: TBP;ILT.2; ABCC5; CD18; GATA3; DICER1; MSH3; GBP1; IRS1; CD3z; fas1; TUBB;BAD; ERCC1; MCM6; PR; APC; GGPS1; KRT18; ESRRG; E2F1; AKT2; A.Catenin;CEGP1; NPD009; MAPK14; RUNX1; ID2; G.Catenin; FBXO5; FHIT; MTA1; ERBB4;FUS; BBC3; IGF1R; CD9; TP53BP1; MUC1; IGFBP5; rhoC; RALBP1; CDC20;STAT3; ERK1; HLA.DPB1; SGCB; CGA; DHPS; MGMT; CRIP2; MMP12; ErbB3;RAP1GDS1; CDC25B; IL6; CCND1; CYBA; PRKCD; DR4; Hepsin; CRABP1;AK055699; Contig.51037; VCAM1; FYN; GRB7; AKAP.2; RASSF1; MCP1; ZNF38;MCM2; GBP2; SEMA3F; CD31; COL1A1; ER2; BAG1; AKT1; COL1A2; STAT1;Wnt.5a; PTPD1; RAB6C; TK1, ErbB2, CCNB1, BIRC5, STK6, MKI67, MYBL2,MMP11, CTSL2, CD68, GSTM1, BCL2, ESR1.

In an embodiment, the array compises polynucleotides hybridizing to aplurality of the following genes: TBP; ILT.2; ABCC5; CD18; GATA3;DICER1; MSH3; GBP1; IRS1; CD3z; fas1; TUBB; BAD; ERCC1; MCM6; PR; APC;GGPS1; KRT18; ESRRG; E2F1; AKT2; A.Catenin; CEGP1; NPD009; MAPK14;RUNX1; ID2; G.Catenin; FBXO5; FHIT; MTA1; ERBB4; FUS; BBC3; IGF1R; CD9;TP53BP1; MUC1; IGFBP5; rhoC; RALBP1; CDC20; STAT3; ERK1; HLA.DPB1; SGCB;CGA; DHPS; MGMT; CRIP2; MMP12; ErbB3; RAP1GDS1; CDC25B; IL6; CCND1;CYBA; PRKCD; DR4; Hepsin; CRABP1; AK055699; Contig.51037; VCAM1; FYN;GRB7; AKAP.2; RASSF1; MCP1; ZNF38; MCM2; GBP2; SEMA3F; CD31; COL1A1;ER2; BAG1; AKT1; COL1A2; STAT1; Wnt.5a; PTPD1; RAB6C; TK1.

In another embodiment, the array comprises polynucleotides hybridizingto a plurality of the following genes: ILT.2; CD18; GBP1; CD3z; fas1;MCM6; E2F1; ID2; FBXO5; CDC20; HLA.DPB1; CGA; MMP12; CDC25B; IL6; CYBA;DR4; CRABP1; Contig.51037; VCAM1; FYN; GRB7; AKAP.2; RASSF1; MCP1; MCM2;GBP2; CD31; ER2; STAT1; TK1; ERBB2, CCNB1, BIRC5, STK6, MKI67, MYBL2,MMP11, CTSL2 and CD68.

In yet another embodiment, the array comprises polynucleotideshybridizing to a plurality of the following genes: ILT.2; CD18; GBP1;CD3z; fas1; MCM6; E2F1; ID2; FBXO5; CDC20; HLA.DPB1; CGA; MMP12; CDC25B;IL6; CYBA; DR4; CRABP1; Contig.51037; VCAM1; FYN; GRB7; AKAP.2; RASSF1;MCP1; MCM2; GBP2; CD31; ER2; STAT1; TK1

In a still further embodiment, the array comprises polynucleotideshybridizing to a plurality of the following genes: TBP; ABCC5; GATA3;DICER1; MSH3; IRS1; TUBB; BAD; ERCC1; PR; APC; GGPS1; KRT18; ESRRG;AKT2; A.Catenin; CEGP1; NPD009; MAPK14; RUNX1; G.Catenin; FHIT; MTA1;ErbB4; FUS; BBC3; IGF1R; CD9; TP53BP1; MUC1; IGFBP5; rhoC; RALBP1;STAT3; ERK1; SGCB; DHPS; MGMT; CRIP2; ErbB3; RAP1GDS1; CCND1; PRKCD;Hepsin; AK055699; ZNF38; SEMA3F; COL1A1; BAG1; AKT1; COL1A2; Wnt.5a;PTPD1; RAB6C; GSTM1, BCL2, ESR1.

In another embodiment, the array comprises polynucleotides hybridizingto a plurality of the following genes: TBP; ABCC5; GATA3; DICER1; MSH3;IRS1; TUBB; BAD; ERCC1; PR; APC; GGPS1; KRT18; ESRRG; AKT2; A.Catenin;CEGP1; NPD009; MAPK14; RUNX1; G.Catenin; FHIT; MTA1; ErbB4; FUS; BBC3;IGF1R; CD9; TP53BP1; MUC1; IGFBP5; rhoC; RALBP1; STAT3; ERK1; SGCB;DHPS; MGMT; CRIP2; ErbB3; RAP1GDS1; CCND; PRKCD; Hepsin; AK055699;ZNF38; SEMA3F; COL1A1; BAG1; AKT1; COL1A2; Wnt.5a; PTPD1; RAB6C.

In various embodiments, the array comprises at least five, or at least10, or at least 15, or at least 10 of such polynucleotides.

In a particular embodiment, the array comprises polynucleotideshybridizing to all of the genes listed above.

In another particular embodiment, the array comprises more than onepolynucleotide hybridizing to the same gene.

In another embodiment, at least one of the polynucleotides comprises anintron-based sequence the expression of which correlates with theexpression of a corresponding exon sequence.

In various embodiments, the polynucleotides can be cDNAs oroligonucleotides.

In another aspect, the invention concerns a method of preparing apersonalized genomics profile for a patient comprising the steps of:

-   -   (a) determining the normalized expression levels of the RNA        transcripts or the expression products of a gene or gene set        selected from the group consisting of TBP; ILT.2; ABCC5; CD18;        GATA3; DICER1; MSH3; GBP1; IRS1; CD3z; fas1; TUBB; BAD; ERCC1;        MCM6; PR; APC; GGPS1; KRT18; ESRRG; E2F1; AKT2; A.Catenin;        CEGP1; NPD009; MAPK14; RUNX1; ID2; G.Catenin; FBXO5; FHIT; MTA1;        ERBB4; FUS; BBC3; IGF1R; CD9; TP53BP1; MUC1; IGFBP5; rhoC;        RALBP1; CDC20; STAT3; ERK1; HLA.DPB1; SGCB; CGA; DHPS; MGMT;        CRIP2; MMP12; ErbB3; RAP1GDS1; CDC25B; IL6; CCND1; CYBA; PRKCD;        DR4; Hepsin; CRABP1; AK055699; Contig.51037; VCAM1; FYN; GRB7;        AKAP.2; RASSF1; MCP1; ZNF38; MCM2; GBP2; SEMA3F; CD31; COL1A1;        ER2; BAG1; AKT1; COL1A2; STAT1; Wnt.5a; PTPD1; RAB6C; TK1,        ErbB2, CCNB1, BIRC5, STK6, MKI67, MYBL2, MMP11, CTSL2, CD68,        GSTM1, BCL2, ESR1, in a cancer cell obtained from said patient;        and    -   (b) creating a report summarizing the data obtained by the gene        expression analysis.

In a specific embodiment, if increased expression of one or more ofILT.2; CD18; GBP1; CD3z; fas1; MCM6; E2F1; ID2; FBXO5; CDC20; HLA.DPB1;CGA; MMP12; CDC25B; IL6; CYBA; DR4; CRABP1; Contig.51037; VCAM1; FYN;GRB7; AKAP.2; RASSF1; MCP1; MCM2; GBP2; CD31; ER2; STAT1; TK1; ERBB2,CCNB1, BIRC5, STK6, MKI67, MYBL2, MMP11, CTSL2 and CD68; or thecorresponding expression product, is determined, the report includes aprediction that said subject has an increased likelihood of response tochemotherapy. In this case, in a particular embodiment, the methodincludes the additional step of treating the patient with achemotherapeutic agent.

In the foregoing method, if increased expression of one or more of TBP;ABCC5; GATA3; DICER1; MSH3; IRS1; TUBB; BAD; ERCC1; PR; APC; GGPS1;KRT18; ESRRG; AKT2; A.Catenin; CEGP1; NPD009; MAPK14; RUNX1; G.Catenin;FHIT; MTA1; ErbB4; FUS; BBC3; IGF1R; CD9; TP53BP1; MUC1; IGFBP5; rhoC;RALBP1; STAT3; ERK1; SGCB; DHPS; MGMT; CRIP2; ErbB3; RAP1GDS1; CCND1;PRKCD; Hepsin; AK055699; ZNF38; SEMA3F; COL1A1; BAG1; AKT1; COL1A2;Wnt.5a; PTPD1; RAB6C; GSTM1, BCL2, ESR1; or the corresponding expressionproduct, is determined, the report includes a prediction that saidsubject has a decreased likelihood of response to chemotherapy.

In another aspect, the invention concerns a method for determining thelikelihood of the response of a patient to chemotherapy, comprising:

-   -   (a) determining the expression levels of the RNA transcripts of        following genes ACTB, BAG1, BCL2, CCNB1, CD68, SCUBE2, CTSL2,        ESR1, GAPD, GRB7, GSTM1, GUSB, ERBB2, MKI67, MYBL2, PGR, RPLPO,        STK6, MMP11, BIRC5, TFRC, or their expression products, and    -   (b) calculating the recurrence score (RS).

In an embodiment, patients having an RS>50 are in the upper 50percentile of patients who are likely to respond to chemotherapy.

In another embodiment, patients having an RS<35 are in the lower 50percentile of patients who are likely to response to chemotherapy. In afurther embodiment, RS is determined by creating the following genesubsets:

-   -   (i) growth factor subset: GRB7 and HER2;    -   (ii) estrogen receptor subset: ER, PR, Bcl2, and CEGP1;    -   (iii) proliferation subset: SURV, Ki.67, MYBL2, CCNB1, and        STK15; and    -   (iv) invasion subset: CTSL2, and STMY3;    -   wherein a gene within any of subsets (i)-(iv) can be substituted        by substitute gene which coexpresses with said gene in said        tumor with a Pearson correlation coefficient of ≧0.40; and    -   (c) calculating the recurrence score (RS) for said subject by        weighting the contributions of each of subsets (i)-(iv), to        breast cancer recurrence.

The foregoing method may further comprise determining the RNAtranscripts of CD68, GSTM1 and BAG1 or their expression products, orcorresponding substitute genes or their expression products, andincluding the contribution of said genes or substitute genes to breastcancer recurrence in calculating the RS

RS may, for example, be determined by using the following equation:RS=(0.23 to 0.70)×GRB7axisthresh−(0.17 to 0.55)×ERaxis+(0.52 to1.56)×prolifaxisthresh+(0.07 to 0.21)×invasionaxis+(0.03 to0.15)×CD68−(0.04 to 0.25)×GSTM1−(0.05 to 0.22)×BAG1wherein

-   -   (i) GRB7 axis=(0.45 to 1.35)×GRB7+(0.05 to 0.15)×HER2;    -   (ii) if GRB7 axis<−2, then GRB7 axis thresh=−2, and if GRB7        axis≧−2, then GRB7 axis thresh=GRB7 axis;    -   (iii) ER axis=(Est1+PR+Bcl2+CEGP1)/4;    -   (iv) prolifaxis=(SURV+Ki.67+MYBL2+CCNB1+STK15)/5;    -   (v) if prolifaxis<−3.5, then prolifaxisthresh=−3.5, if        prolifaxis≧−3.5, then prolifaxishresh=prolifaxis; and    -   (vi) invasionaxis=(CTSL2+STMY3)/2,

wherein the individual contributions of the genes in (iii), (iv) and(vi) are weighted by a factor of 0.5 to 1.5, and wherein a higher RSrepresents an increased likelihood of breast cancer recurrence.

In another embodiment, RS is determined by using the following equation:

RS(range, 0 − 100) = +0.47 × HER2  Group  Score − 0.34 × ER  Group  Score + 1.04 × Proliferation  Group  Score + 0.10 × Invasion  Group  Score + 0.05 × CD68 − 0.08 × GSTM1 − 0.07 × BAG1

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows the relationship between recurrence score (RS) andlikelihood of patient response to chemotherapy, based on results from aclinical trial with pathologic complete response endpoint.

Table 1 shows a list of genes, the expression of which correlates,positively or negatively, with breast cancer response to adriamycin andtaxane neoadjuvant chemotherapy. Results from a clinical trial withpathologic complete response endpoint. Statistical analysis utilizedunivarite generalized linear models with a probit link function.

Table 2 presents a list of genes, the expression of which predictsbreast cancer response to chemotherapy. Results from a retrospectiveclinical trial. The table includes accession numbers for the genes,sequences for the forward and reverse primers (designated by “f” and“r”, respectively) and probes (designated by “p”) used for PCRamplification.

Table 3 shows the amplicon sequences used in PCR amplification of theindicated genes.

DETAILED DESCRIPTION A. Definitions

Unless defined otherwise, technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Singleton et al., Dictionary ofMicrobiology and Molecular Biology 2nd ed., J. Wiley & Sons (New York,N.Y. 1994), and March, Advanced Organic Chemistry Reactions, Mechanismsand Structure 4th ed., John Wiley & Sons (New York, N.Y. 1992), provideone skilled in the art with a general guide to many of the terms used inthe present application.

One skilled in the art will recognize many methods and materials similaror equivalent to those described herein, which could be used in thepractice of the present invention. Indeed, the present invention is inno way limited to the methods and materials described. For purposes ofthe present invention, the following terms are defined below.

The term “microarray” refers to an ordered arrangement of hybridizablearray elements, preferably polynucleotide probes, on a substrate.

The term “polynucleotide,” when used in singular or plural, generallyrefers to any polyribonucleotide or polydeoxribonucleotide, which may beunmodified RNA or DNA or modified RNA or DNA. Thus, for instance,polynucleotides as defined herein include, without limitation, single-and double-stranded DNA, DNA including single- and double-strandedregions, single- and double-stranded RNA, and RNA including single- anddouble-stranded regions, hybrid molecules comprising DNA and RNA thatmay be single-stranded or, more typically, double-stranded or includesingle- and double-stranded regions. In addition, the term“polynucleotide” as used herein refers to triple-stranded regionscomprising RNA or DNA or both RNA and DNA. The strands in such regionsmay be from the same molecule or from different molecules. The regionsmay include all of one or more of the molecules, but more typicallyinvolve only a region of some of the molecules. One of the molecules ofa triple-helical region often is an oligonucleotide. The term“polynucleotide” specifically includes cDNAs. The term includes DNAs(including cDNAs) and RNAs that contain one or more modified bases.Thus, DNAs or RNAs with backbones modified for stability or for otherreasons are “polynucleotides” as that term is intended herein. Moreover,DNAs or RNAs comprising unusual bases, such as inosine, or modifiedbases, such as tritiated bases, are included within the term“polynucleotides” as defined herein. In general, the term“polynucleotide” embraces all chemically, enzymatically and/ormetabolically modified forms of unmodified polynucleotides, as well asthe chemical forms of DNA and RNA characteristic of viruses and cells,including simple and complex cells.

The term “oligonucleotide” refers to a relatively short polynucleotide,including, without limitation, single-stranded deoxyribonucleotides,single- or double-stranded ribonucleotides, RNA:DNA hybrids anddouble-stranded DNAs. Oligonucleotides, such as single-stranded DNAprobe oligonucleotides, are often synthesized by chemical methods, forexample using automated oligonucleotide synthesizers that arecommercially available. However, oligonucleotides can be made by avariety of other methods, including in vitro recombinant DNA-mediatedtechniques and by expression of DNAs in cells and organisms.

The terms “differentially expressed gene,” “differential geneexpression” and their synonyms, which are used interchangeably, refer toa gene whose expression is activated to a higher or lower level in asubject suffering from a disease, specifically cancer, such as breastcancer, relative to its expression in a normal or control subject. Theterms also include genes whose expression is activated to a higher orlower level at different stages of the same disease. It is alsounderstood that a differentially expressed gene may be either activatedor inhibited at the nucleic acid level or protein level, or may besubject to alternative splicing to result in a different polypeptideproduct. Such differences may be evidenced by a change in mRNA levels,surface expression, secretion or other partitioning of a polypeptide,for example. Differential gene expression may include a comparison ofexpression between two or more genes or their gene products, or acomparison of the ratios of the expression between two or more genes ortheir gene products, or even a comparison of two differently processedproducts of the same gene, which differ between normal subjects andsubjects suffering from a disease, specifically cancer, or betweenvarious stages of the same disease. Differential expression includesboth quantitative, as well as qualitative, differences in the temporalor cellular expression pattern in a gene or its expression productsamong, for example, normal and diseased cells, or among cells which haveundergone different disease events or disease stages. For the purpose ofthis invention, “differential gene expression” is considered to bepresent when there is at least an about two-fold, preferably at leastabout four-fold, more preferably at least about six-fold, mostpreferably at least about ten-fold difference between the expression ofa given gene in normal and diseased subjects, or in various stages ofdisease development in a diseased subject.

The term “normalized” with regard to a gene transcript or a geneexpression product refers to the level of the transcript or geneexpression product relative to the mean levels of transcripts/productsof a set of reference genes, wherein the reference genes are eitherselected based on their minimal variation across, patients, tissues ortreatments (“housekeeping genes”), or the reference genes are thetotality of tested genes. In the latter case, which is commonly referredto as “global normalization”, it is important that the total number oftested genes be relatively large, preferably greater than 50.Specifically, the term ‘normalized’ with respect to an RNA transcriptrefers to the transcript level relative to the mean of transcript levelsof a set of reference genes. More specifically, the mean level of an RNAtranscript as measured by TaqMan® RT-PCR refers to the Ct value minusthe mean Ct values of a set of reference gene transcripts.

The terms “expression threshold,” and “defined expression threshold” areused interchangeably and refer to the level of a gene or gene product inquestion above which the gene or gene product serves as a predictivemarker for patient response or resistance to a drug. The thresholdtypically is defined experimentally from clinical studies. Theexpression threshold can be selected either for maximum sensitivity (forexample, to detect all responders to a drug), or for maximum selectivity(for example to detect only responders to a drug), or for minimum error.

The phrase “gene amplification” refers to a process by which multiplecopies of a gene or gene fragment are formed in a particular cell orcell line. The duplicated region (a stretch of amplified DNA) is oftenreferred to as “amplicon.” Often, the amount of the messenger RNA (mRNA)produced, i.e., the level of gene expression, also increases in theproportion to the number of copies made of the particular gene.

The term “prognosis” is used herein to refer to the prediction of thelikelihood of cancer-attributable death or progression, includingrecurrence, metastatic spread, and drug resistance, of a neoplasticdisease, such as breast cancer. The term “prediction” is used herein torefer to the likelihood that a patient will respond either favorably orunfavorably to a drug or set of drugs, and also the extent of thoseresponses, or that a patient will survive, following surgical removal orthe primary tumor and/or chemotherapy for a certain period of timewithout cancer recurrence. The predictive methods of the presentinvention can be used clinically to make treatment decisions by choosingthe most appropriate treatment modalities for any particular patient.The predictive methods of the present invention are valuable tools inpredicting if a patient is likely to respond favorably to a treatmentregimen, such as surgical intervention, chemotherapy with a given drugor drug combination, and/or radiation therapy, or whether long-termsurvival of the patient, following surgery and/or termination ofchemotherapy or other treatment modalities is likely.

The term “long-term” survival is used herein to refer to survival for atleast 3 years, more preferably for at least 8 years, most preferably forat least 10 years following surgery or other treatment.

The term “tumor,” as used herein, refers to all neoplastic cell growthand proliferation, whether malignant or benign, and all pre-cancerousand cancerous cells and tissues.

The terms “cancer” and “cancerous” refer to or describe thephysiological condition in mammals that is typically characterized byunregulated cell growth. Examples of cancer include but are not limitedto, breast cancer, colon cancer, lung cancer, prostate cancer,hepatocellular cancer, gastric cancer, pancreatic cancer, cervicalcancer, ovarian cancer, liver cancer, bladder cancer, cancer of theurinary tract, thyroid cancer, renal cancer, carcinoma, melanoma, andbrain cancer.

The “pathology” of cancer includes all phenomena that compromise thewell-being of the patient. This includes, without limitation, abnormalor uncontrollable cell growth, metastasis, interference with the normalfunctioning of neighboring cells, release of cytokines or othersecretory products at abnormal levels, suppression or aggravation ofinflammatory or immunological response, neoplasia, premalignancy,malignancy, invasion of surrounding or distant tissues or organs, suchas lymph nodes, etc.

“Patient response” can be assessed using any endpoint indicating abenefit to the patient, including, without limitation, (1) inhibition,to some extent, of tumor growth, including slowing down and completegrowth arrest; (2) reduction in the number of tumor cells; (3) reductionin tumor size; (4) inhibition (i.e., reduction, slowing down or completestopping) of tumor cell infiltration into adjacent peripheral organsand/or tissues; (5) inhibition (i.e. reduction, slowing down or completestopping) of metastasis; (6) enhancement of anti-tumor immune response,which may, but does not have to, result in the regression or rejectionof the tumor; (7) relief, to some extent, of one or more symptomsassociated with the tumor; (8) increase in the length of survivalfollowing treatment; and/or (9) decreased mortality at a given point oftime following treatment.

“Neoadjuvant therapy” is adjunctive or adjuvant therapy given prior tothe primary (main) therapy. Neoadjuvant therapy includes, for example,chemotherapy, radiation therapy, and hormone therapy. Thus, chemotherapymay be administered prior to surgery to shrink the tumor, so thatsurgery can be more effective, or, in the case of previously unoperabletumors, possible.

“Stringency” of hybridization reactions is readily determinable by oneof ordinary skill in the art, and generally is an empirical calculationdependent upon probe length, washing temperature, and saltconcentration. In general, longer probes require higher temperatures forproper annealing, while shorter probes need lower temperatures.Hybridization generally depends on the ability of denatured DNA toreanneal when complementary strands are present in an environment belowtheir melting temperature. The higher the degree of desired homologybetween the probe and hybridizable sequence, the higher the relativetemperature which can be used. As a result, it follows that higherrelative temperatures would tend to make the reaction conditions morestringent, while lower temperatures less so. For additional details andexplanation of stringency of hybridization reactions, see Ausubel etal., Current Protocols in Molecular Biology, Wiley IntersciencePublishers, (1995).

“Stringent conditions” or “high stringency conditions”, as definedherein, typically: (1) employ low ionic strength and high temperaturefor washing, for example 0.015 M sodium chloride/0.0015 M sodiumcitrate/0.1% sodium dodecyl sulfate at 50° C.; (2) employ duringhybridization a denaturing agent, such as formamide, for example, 50%(v/v) formamide with 0.1% bovine serum albumin/0.1% Ficoll/0.1%polyvinylpyrrolidone/50 mM sodium phosphate buffer at pH 6.5 with 750 mMsodium chloride, 75 mM sodium citrate at 42° C.; or (3) employ 50%formamide, 5×SSC (0.75 M NaCl, 0.075 M sodium citrate), 50 mM sodiumphosphate (pH 6.8), 0.1% sodium pyrophosphate, 5×Denhardt's solution,sonicated salmon sperm DNA (50 μg/ml), 0.1% SDS, and 10% dextran sulfateat 42° C., with washes at 42° C. in 0.2×SSC (sodium chloride/sodiumcitrate) and 50% formamide at 55° C., followed by a high-stringency washconsisting of 0.1×SSC containing EDTA at 55° C.

“Moderately stringent conditions” may be identified as described bySambrook et al., Molecular Cloning: A Laboratory Manual, New York: ColdSpring Harbor Press, 1989, and include the use of washing solution andhybridization conditions (e.g., temperature, ionic strength and % SDS)less stringent that those described above. An example of moderatelystringent conditions is overnight incubation at 37° C. in a solutioncomprising: 20% formamide, 5×SSC (150 mM NaCl, 15 mM trisodium citrate),50 mM sodium phosphate (pH 7.6), 5×Denhardt's solution, 10% dextransulfate, and 20 mg/ml denatured sheared salmon sperm DNA, followed bywashing the filters in 1×SSC at about 37-50° C. The skilled artisan willrecognize how to adjust the temperature, ionic strength, etc. asnecessary to accommodate factors such as probe length and the like.

In the context of the present invention, reference to “at least one,”“at least two,” “at least five,” etc. of the genes listed in anyparticular gene set means any one or any and all combinations of thegenes listed.

B. Detailed Description

The practice of the present invention will employ, unless otherwiseindicated, conventional techniques of molecular biology (includingrecombinant techniques), microbiology, cell biology, and biochemistry,which are within the skill of the art. Such techniques are explainedfully in the literature, such as, “Molecular Cloning: A LaboratoryManual”, 2^(nd) edition (Sambrook et al., 1989); “OligonucleotideSynthesis” (M. J. Gait, ed., 1984); “Animal Cell Culture” (R. I.Freshney, ed., 1987); “Methods in Enzymology” (Academic Press, Inc.);“Handbook of Experimental Immunology”, 4^(th) edition (D. M. Weir & C.C. Blackwell, eds., Blackwell Science Inc., 1987); “Gene TransferVectors for Mammalian Cells” (J. M. Miller & M. P. Calos, eds., 1987);“Current Protocols in Molecular Biology” (F. M. Ausubel et al., eds.,1987); and “PCR: The Polymerase Chain Reaction”, (Mullis et al., eds.,1994).

1. Gene Expression Profiling

Methods of gene expression profiling include methods based onhybridization analysis of polynucleotides, methods based on sequencingof polynucleotides, and proteomics-based methods. The most commonly usedmethods known in the art for the quantification of mRNA expression in asample include northern blotting and in situ hybridization (Parker &Barnes, Methods in Molecular Biology 106:247-283 (1999)); RNAseprotection assays (Hod, Biotechniques 13:852-854 (1992)); and PCR-basedmethods, such as reverse transcription polymerase chain reaction(RT-PCR) (Weis et al., Trends in Genetics 8:263-264 (1992)).Alternatively, antibodies may be employed that can recognize specificduplexes, including DNA duplexes, RNA duplexes, and DNA-RNA hybridduplexes or DNA-protein duplexes. Representative methods forsequencing-based gene expression analysis include Serial Analysis ofGene Expression (SAGE), and gene expression analysis by massivelyparallel signature sequencing (MPSS).

2. PCR-Based Gene Expression Profiling Methods

a. Reverse Transcriptase PCR (RT-PCR)

One of the most sensitive and most flexible quantitative PCR-based geneexpression profiling methods is RT-PCR, which can be used to comparemRNA levels in different sample populations, in normal and tumortissues, with or without drug treatment, to characterize patterns ofgene expression, to discriminate between closely related mRNAs, and toanalyze RNA structure.

The first step is the isolation of mRNA from a target sample. Thestarting material is typically total RNA isolated from human tumors ortumor cell lines; and corresponding normal tissues or cell lines,respectively. Thus RNA can be isolated from a variety of primary tumors,including breast, lung, colon, prostate, brain, liver, kidney, pancreas,spleen, thymus, testis, ovary, uterus, etc., tumor, or tumor cell lines,with pooled DNA from healthy donors. If the source of mRNA is a primarytumor, mRNA can be extracted, for example, from frozen or archivedparaffin-embedded and fixed (e.g. formalin-fixed) tissue samples.

General methods for mRNA extraction are well known in the art and aredisclosed in standard textbooks of molecular biology, including Ausubelet al., Current Protocols of Molecular Biology, John Wiley and Sons(1997). Methods for RNA extraction from paraffin embedded tissues aredisclosed, for example, in Rupp and Locker, Lab Invest. 56:A67 (1987),and De Andrés et al., BioTechniques 18:42044 (1995). In particular, RNAisolation can be performed using purification kit, buffer set andprotease from commercial manufacturers, such as Qiagen, according to themanufacturer's instructions. For example, total RNA from cells inculture can be isolated using Qiagen RNeasy mini-columns. Othercommercially available RNA isolation kits include MasterPure™ CompleteDNA and RNA Purification Kit (EPICENTRE®, Madison, Wis.), and ParaffinBlock RNA Isolation Kit (Ambion, Inc.). Total RNA from tissue samplescan be isolated using RNA Stat-60 (Tel-Test). RNA prepared from tumorcan be isolated, for example, by cesium chloride density gradientcentrifugation.

As RNA cannot serve as a template for PCR, the first step in geneexpression profiling by RT-PCR is the reverse transcription of the RNAtemplate into cDNA, followed by its exponential amplification in a PCRreaction. The two most commonly used reverse transcriptases are avilomyeloblastosis virus reverse transcriptase (AMV-RT) and Moloney murineleukemia virus reverse transcriptase (MMLV-RT). The reversetranscription step is typically primed using specific primers, randomhexamers, or oligo-dT primers, depending on the circumstances and thegoal of expression profiling. For example, extracted RNA can bereverse-transcribed using a GeneAmp RNA PCR kit (Perkin Elmer, Calif.,USA), following the manufacturer's instructions. The derived cDNA canthen be used as a template in the subsequent PCR reaction.

Although the PCR step can use a variety of thermostable DNA-dependentDNA polymerases, it typically employs the Taq DNA polymerase, which hasa 5′-3′ nuclease activity but lacks a 3′-5′ proofreading endonucleaseactivity. Thus, TaqMan® PCR typically utilizes the 5′-nuclease activityof Taq or Tth polymerase to hydrolyze a hybridization probe bound to itstarget amplicon, but any enzyme with equivalent 5′ nuclease activity canbe used. Two oligonucleotide primers are used to generate an amplicontypical of a PCR reaction. A third oligonucleotide, or probe, isdesigned to detect nucleotide sequence located between the two PCRprimers. The probe is non-extendible by Taq DNA polymerase enzyme, andis labeled with a reporter fluorescent dye and a quencher fluorescentdye. Any laser-induced emission from the reporter dye is quenched by thequenching dye when the two dyes are located close together as they areon the probe. During the amplification reaction, the Taq DNA polymeraseenzyme cleaves the probe in a template-dependent manner. The resultantprobe fragments disassociate in solution, and signal from the releasedreporter dye is free from the quenching effect of the secondfluorophore. One molecule of reporter dye is liberated for each newmolecule synthesized, and detection of the unquenched reporter dyeprovides the basis for quantitative interpretation of the data.

TaqMan® RT-PCR can be performed using commercially available equipment,such as, for example, ABI PRISM 7700™ Sequence Detection System™(Perkin-Elmer-Applied Biosystems, Foster City, Calif., USA), orLightcycler (Roche Molecular Biochemicals, Mannheim, Germany). In apreferred embodiment, the 5′ nuclease procedure is run on a real-timequantitative PCR device such as the ABI PRISM 7700™ Sequence DetectionSystem™. The system consists of a thermocycler, laser, charge-coupleddevice (CCD), camera and computer. The system amplifies samples in a96-well format on a thermocycler. During amplification, laser-inducedfluorescent signal is collected in real-time through fiber optics cablesfor all 96 wells, and detected at the CCD. The system includes softwarefor running the instrument and for analyzing the data.

5′-Nuclease assay data are initially expressed as Ct, or the thresholdcycle. As discussed above, fluorescence values are recorded during everycycle and represent the amount of product amplified to that point in theamplification reaction. The point when the fluorescent signal is firstrecorded as statistically significant is the threshold cycle (C_(t)).

To minimize errors and the effect of sample-to-sample variation, RT-PCRis usually performed using a reference RNA which ideally is expressed ata constant level among different tissues, and is unaffected by theexperimental treatment. RNAs most frequently used to normalize patternsof gene expression are mRNAs for the housekeeping genesglyceraldehyde-3-phosphate-dehydrogenase (GAPD) and β-actin (ACTB).

A more recent variation of the RT-PCR technique is the real timequantitative PCR, which measures PCR product accumulation through adual-labeled fluorigenic probe (i.e., TaqMan® probe). Real time PCR iscompatible both with quantitative competitive PCR, where internalcompetitor for each target sequence is used for normalization, and withquantitative comparative PCR using a normalization gene contained withinthe sample, or a housekeeping gene for RT-PCR. For further details see,e.g. Held et al., Genome Research 6:986-994 (1996).

b. MassARRAY System

In the MassARRAY-based gene expression profiling method, developed bySequenom, Inc. (San Diego, Calif.) following the isolation of RNA andreverse transcription, the obtained cDNA is spiked with a synthetic DNAmolecule (competitor), which matches the targeted cDNA region in allpositions, except a single base, and serves as an internal standard. ThecDNA/competitor mixture is PCR amplified and is subjected to a post-PCRshrimp alkaline phosphatase (SAP) enzyme treatment, which results in thedephosphorylation of the remaining nucleotides. After inactivation ofthe alkaline phosphatase, the PCR products from the competitor and cDNAare subjected to primer extension, which generates distinct mass signalsfor the competitor- and cDNA-derives PCR products. After purification,these products are dispensed on a chip array, which is pre-loaded withcomponents needed for analysis with matrix-assisted laser desorptionionization time-of-flight mass spectrometry (MALDI-TOF MS) analysis. ThecDNA present in the reaction is then quantified by analyzing the ratiosof the peak areas in the mass spectrum generated. For further detailssee, e.g. Ding and Cantor, Proc. Natl. Acad. Sci. USA 100:3059-3064(2003).

c. Other PCR-Based Methods

Further PCR-based techniques include, for example, differential display(Liang and Pardee, Science 257:967-971 (1992)); amplified fragmentlength polymorphism (iAFLP) (Kawamoto et al., Genome Res. 12:1305-1312(1999)); BeadArray™ technology (Illumina, San Diego, Calif.; Oliphant etal., Discovery of Markers for Disease (Supplement to Biotechniques),June 2002; Ferguson et al., Analytical Chemistry 72:5618 (2000));BeadsArray for Detection of Gene Expression (BADGE), using thecommercially available Luminex¹⁰⁰ LabMAP system and multiple color-codedmicrospheres (Luminex Corp., Austin, Tex.) in a rapid assay for geneexpression (Yang et al., Genome Res. 11:1888-1898 (2001)); and highcoverage expression profiling (HiCEP) analysis (Fukumura et al., Nucl.Acids. Res. 31(16) e94 (2003)).

3. Microarrays

Differential gene expression can also be identified, or confirmed usingthe microarray technique. Thus, the expression profile of breastcancer-associated genes can be measured in either fresh orparaffin-embedded tumor tissue, using microarray technology. In thismethod, polynucleotide sequences of interest (including cDNAs andoligonucleotides) are plated, or arrayed, on a microchip substrate. Thearrayed sequences are then hybridized with specific DNA probes fromcells or tissues of interest. Just as in the RT-PCR method, the sourceof mRNA typically is total RNA isolated from human tumors or tumor celllines, and corresponding normal tissues or cell lines. Thus RNA can beisolated from a variety of primary tumors or tumor cell lines. If thesource of mRNA is a primary tumor, mRNA can be extracted, for example,from frozen or archived paraffin-embedded and fixed (e.g.formalin-fixed) tissue samples, which are routinely prepared andpreserved in everyday clinical practice.

In a specific embodiment of the microarray technique, PCR amplifiedinserts of cDNA clones are applied to a substrate in a dense array.Preferably at least 10,000 nucleotide sequences are applied to thesubstrate. The microarrayed genes, immobilized on the microchip at10,000 elements each, are suitable for hybridization under stringentconditions. Fluorescently labeled cDNA probes may be generated throughincorporation of fluorescent nucleotides by reverse transcription of RNAextracted from tissues of interest. Labeled cDNA probes applied to thechip hybridize with specificity to each spot of DNA on the array. Afterstringent washing to remove non-specifically bound probes, the chip isscanned by confocal laser microscopy or by another detection method,such as a CCD camera. Quantitation of hybridization of each arrayedelement allows for assessment of corresponding mRNA abundance. With dualcolor fluorescence, separately labeled cDNA probes generated from twosources of RNA are hybridized pairwise to the array. The relativeabundance of the transcripts from the two sources corresponding to eachspecified gene is thus determined simultaneously. The miniaturized scaleof the hybridization affords a convenient and rapid evaluation of theexpression pattern for large numbers of genes. Such methods have beenshown to have the sensitivity required to detect rare transcripts, whichare expressed at a few copies per cell, and to reproducibly detect atleast approximately two-fold differences in the expression levels(Schena et al., Proc. Natl. Acad. Sci. USA 93(2):106-149 (1996)).Microarray analysis can be performed by commercially availableequipment, following manufacturer's protocols, such as by using theAffymetrix GenChip technology, or Incyte's microarray technology.

The development of microarray methods for large-scale analysis of geneexpression makes it possible to search systematically for molecularmarkers of cancer classification and outcome prediction in a variety oftumor types.

4. Serial Analysis of Gene Expression (SAGE)

Serial analysis of gene expression (SAGE) is a method that allows thesimultaneous and quantitative analysis of a large number of genetranscripts, without the need of providing an individual hybridizationprobe for each transcript. First, a short sequence tag (about 10-14 bp)is generated that contains sufficient information to uniquely identify atranscript, provided that the tag is obtained from a unique positionwithin each transcript. Then, many transcripts are linked together toform long serial molecules, that can be sequenced, revealing theidentity of the multiple tags simultaneously. The expression pattern ofany population of transcripts can be quantitatively evaluated bydetermining the abundance of individual tags, and identifying the genecorresponding to each tag. For more details see, e.g. Velculescu et al.,Science 270:484-487 (1995); and Velculescu et al., Cell 88:243-51(1997).

5. Gene Expression Analysis by Massively Parallel Signature Sequencing(MPSS)

This method, described by Brenner et al., Nature Biotechnology18:630-634 (2000), is a sequencing approach that combines non-gel-basedsignature sequencing with in vitro cloning of millions of templates onseparate 5 μm diameter microbeads. First, a microbead library of DNAtemplates is constructed by in vitro cloning. This is followed by theassembly of a planar array of the template-containing microbeads in aflow cell at a high density (typically greater than 3×10⁶microbeads/cm²). The free ends of the cloned templates on each microbeadare analyzed simultaneously, using a fluorescence-based signaturesequencing method that does not require DNA fragment separation. Thismethod has been shown to simultaneously and accurately provide, in asingle operation, hundreds of thousands of gene signature sequences froma yeast cDNA library.

6. Immunohistochemistry

Immunohistochemistry methods are also suitable for detecting theexpression levels of the prognostic markers of the present invention.Thus, antibodies or antisera, preferably polyclonal antisera, and mostpreferably monoclonal antibodies specific for each marker are used todetect expression. The antibodies can be detected by direct labeling ofthe antibodies themselves, for example, with radioactive labels,fluorescent labels, hapten labels such as, biotin, or an enzyme such ashorse radish peroxidase or alkaline phosphatase. Alternatively,unlabeled primary antibody is used in conjunction with a labeledsecondary antibody, comprising antisera, polyclonal antisera or amonoclonal antibody specific for the primary antibody.Immunohistochemistry protocols and kits are well known in the art andare commercially available.

7. Proteomics

The term “proteome” is defined as the totality of the proteins presentin a sample (e.g. tissue, organism, or cell culture) at a certain pointof time. Proteomics includes, among other things, study of the globalchanges of protein expression in a sample (also referred to as“expression proteomics”). Proteomics typically includes the followingsteps: (1) separation of individual proteins in a sample by 2-D gelelectrophoresis (2-D PAGE); (2) identification of the individualproteins recovered from the gel, e.g. my mass spectrometry or N-terminalsequencing, and (3) analysis of the data using bioinformatics.Proteomics methods are valuable supplements to other methods of geneexpression profiling, and can be used, alone or in combination withother methods, to detect the products of the prognostic markers of thepresent invention.

8. General Description of mRNA Isolation, Purification and Amplification

The steps of a representative protocol for profiling gene expressionusing fixed, paraffin-embedded tissues as the RNA source, including mRNAisolation, purification, primer extension and amplification are given invarious published journal articles (for example: T. E. Godfrey et al. J.Molec. Diagnostics 2: 84-91 [2000]; K. Specht et al., Am. J. Pathol.158: 419-29 [2001]). Briefly, a representative process starts withcutting about 10 μm thick sections of paraffin-embedded tumor tissuesamples. The RNA is then extracted, and protein and DNA are removed.After analysis of the RNA concentration, RNA repair and/or amplificationsteps may be included, if necessary, and RNA is reverse transcribedusing gene specific promoters followed by RT-PCR. Finally, the data areanalyzed to identify the best treatment option(s) available to thepatient on the basis of the characteristic gene expression patternidentified in the tumor sample examined.

9. Cancer Chemotherapy

Chemotherapeutic agents used in cancer treatment can be divided intoseveral groups, depending on their mechanism of action. Somechemotherapeutic agents directly damage DNA and RNA. By disruptingreplication of the DNA such chemotherapeutics either completely haltreplication, or result in the production of nonsense DNA or RNA. Thiscategory includes, for example, cisplatin (Platinol®), daunorubicin(Cerubidine®), doxorubicin (Adriamycin®), and etoposide (VePesid®).Another group of cancer chemotherapeutic agents interfere with theformation of nucleotides or deoxyribonucleotides, so that RNA synthesisand cell replication is blocked. Examples of drugs in this class includemethotrexate (Abitrexate®), mercaptopurine (Purinethol®), fluorouracil(Adrucil®)), and hydroxyurea (Hydrea®). A third class ofchemotherapeutic agents effects the synthesis or breakdown of mitoticspindles, and, as a result, interrupt cell division. Examples of drugsin this class include Vinblastine (Velban®), Vincristine (Oncovin®) andtaxenes, such as, Pacitaxel (Taxol®), and Tocetaxel (Taxotere®)Tocetaxel is currently approved in the United States to treat patientswith locally advanced or metastatic breast cancer after failure of priorchemotherapy, and patients with locally advanced or metastatic non-smallcell lung cancer after failure of prior platinum-based chemotherapy.

A common problem with chemotherapy is the high toxicity ofchemotherapeutic agents, such as anthracyclines and taxenes, whichlimits the clinical benefits of this treatment approach.

Most patients receive chemotherapy immediately following surgicalremoval of tumor. This approach is commonly referred to as adjuvanttherapy. However, chemotherapy can be administered also before surgery,as so called neoadjuvant treatment. Although the use of neo-adjuvantchemotherapy originates from the treatment of advanced and inoperablebreast cancer, it has gained acceptance in the treatment of other typesof cancers as well. The efficacy of neoadjuvant chemotherapy has beentested in several clinical trials. In the multi-center National SurgicalAdjuvant Breast and Bowel Project B-18 (NSAB B-18) trial (Fisher et al.,J. Clin. Oncology 15:2002-2004 (1997); Fisher et al., J. Clin. Oncology16:2672-2685 (1998)) neoadjuvant therapy was performed with acombination of adriamycin and cyclophosphamide (“AC regimen”). Inanother clinical trial, neoadjuvant therapy was administered using acombination of 5-fluorouracil, epirubicin and cyclophosphamide (“FECregimen”) (van Der Hage et al., J. Clin. Oncol. 19:4224-4237 (2001)).Newer clinical trials have also used taxane-containing neoadjuvanttreatment regiments. See, e.g. Holmes et al., J. Natl. Cancer Inst.83:1797-1805 (1991) and Moliterni et al., Seminars in Oncology,24:S17-10-S-17-14 (1999). For further information about neoadjuvantchemotherapy for breast cancer see, Cleator et al., Endocrine-RelatedCancer 9:183-195 (2002).

10. Cancer Gene Set, Assayed Gene Subsequences, and Clinical Applicationof Gene Expression Data

An important aspect of the present invention is to use the measuredexpression of certain genes by breast cancer tissue to provideprognostic information. For this purpose it is necessary to correct for(normalize away) differences in the amount of RNA assayed, variabilityin the quality of the RNA used, and other factors, such as machine andoperator differences. Therefore, the assay typically measures andincorporates the use of reference RNAs, including those transcribed fromwell-known housekeeping genes, such as GAPD and ACTB. A precise methodfor normalizing gene expression data is given in “User Bulletin #2” forthe ABI PRISM 7700 Sequence Detection System (Applied Biosystems; 1997).Alternatively, normalization can be based on the mean or median signal(Ct) of all of the assayed genes or a large subset thereof (globalnormalization approach). In the study described in the followingExample, a so called central normalization strategy was used, whichutilized a subset of the screened genes, selected based on lack ofcorrelation with clinical outcome, for normalization.

11. Recurrence and Response to Therapy Scores and Their Applications

Copending application Ser. No. 60/486,302, filed on Jul. 10, 2003,describes an algorithm-based prognostic test for determining thelikelihood of cancer recurrence and/or the likelihood that a patientresponds well to a treatment modality. Features of the algorithm thatdistinguish it from other cancer prognostic methods include: 1) a uniqueset of test mRNAs (or the corresponding gene expression products) usedto determine recurrence likelihood, 2) certain weights used to combinethe expression data into a formula, and 3) thresholds used to dividepatients into groups of different levels of risk, such as low, medium,and high risk groups. The algorithm yields a numerical recurrence score(RS) or, if patient response to treatment is assessed, response totherapy score (RTS).

The test requires a laboratory assay to measure the levels of thespecified mRNAs or their expression products, but can utilize very smallamounts of either fresh tissue, or frozen tissue or fixed,paraffin-embedded tumor biopsy specimens that have already beennecessarily collected from patients and archived. Thus, the test can benoninvasive. It is also compatible with several different methods oftumor tissue harvest, for example, via core biopsy or fine needleaspiration.

According to the method, cancer recurrence score (RS) is determined by:

(a) subjecting a biological sample comprising cancer cells obtained fromsaid subject to gene or protein expression profiling;

(b) quantifying the expression level of multiple individual genes [i.e.,levels of mRNAs or proteins] so as to determine an expression value foreach gene;

(c) creating subsets of the gene expression values, each subsetcomprising expression values for genes linked by a cancer-relatedbiological function and/or by co-expression;

(d) multiplying the expression level of each gene within a subset by acoefficient reflecting its relative contribution to cancer recurrence orresponse to therapy within said subset and adding the products ofmultiplication to yield a term for said subset;

(e) multiplying the term of each subset by a factor reflecting itscontribution to cancer recurrence or response to therapy; and

(f) producing the sum of terms for each subset multiplied by said factorto produce a recurrence score (RS) or a response to therapy (RTS) score,

wherein the contribution of each subset which does not show a linearcorrelation with cancer recurrence or response to therapy is includedonly above a predetermined threshold level, and

wherein the subsets in which increased expression of the specified genesreduce risk of cancer recurrence are assigned a negative value, and thesubsets in which expression of the specified genes increase risk ofcancer recurrence are assigned a positive value.

In a particular embodiment, RS is determined by:

(a) determining the expression levels of GRB7, HER2, EstR1, PR, Bcl2,CEGP1, SURV, Ki.67, MYBL2, CCNB1, STK15, CTSL2, STMY3, CD68, GSTM1, andBAG1, or their expression products, in a biological sample containingtumor cells obtained from said subject; and

(b) calculating the recurrence score (RS) by the following equation:RS=(0.23 to 0.70)×GRB7axisthresh−(0.17 to 0.51)×ERaxis+(0.53 to1.56)×prolifaxisthresh+(0.07 to 0.21)×invasionaxis+(0.03 to0.15)×CD68−(0.04 to 0.25)×GSTM1−(0.05 to 0.22)×BAG1

wherein

-   -   (i) GRB7 axis=(0.45 to 1.35)×GRB7+(0.05 to 0.15)×HER2;    -   (ii) if GRB7 axis<−2, then GRB7 axis thresh=−2, and if GRB7        axis≧−2, then GRB7 axis thresh=GRB7 axis;    -   (iii) ER axis=(Est1+PR+Bcl2+CEGP1)/4;    -   (iv) prolifaxis=(SURV+Ki.67+MYBL2+CCNB1+STK15)/5;    -   (v) if prolifaxis<−3.5, then prolifaxisthresh=−3.5, if        prolifaxis≧−3.5, then prolifaxishresh=prolifaxis; and    -   (vi) invasionaxis=(CTSL2+STMY3)/2,

wherein the terms for all individual genes for which ranges are notspecifically shown can vary between about 0.5 and 1.5, and wherein ahigher RS represents an increased likelihood of cancer recurrence.

Further details of the invention will be described in the followingnon-limiting Example.

EXAMPLE A Retrospective Study of Neoadjuvant Chemotherapy in InvasiveBreast Cancer: Gene Expression Profiling of Paraffin-Embedded CoreBiopsy Tissue

This was a collaborative study involving Genomic Health, Inc., (RedwoodCity Calif.), and Institute Tumori, Milan, Italy. The primary objectiveof the study was to explore the correlation between pre-treatmentmolecular profiles and pathologic complete response (pCR) to neoadjuvantchemotherapy in locally advanced breast cancer.

Patient Inclusion Criteria:

Histologic diagnosis of invasive breast cancer (date of surgery1998-2002); diagnosis of locally advanced breast cancer defined by skininfiltration and or N2 axillary status and or homolateralsupraclavicular positive nodes; core biopsy, neoadjuvant chemotherapyand surgical resection performed at Istituto Nazionale Tumori, Milan;signed informed consent that the biological material obtained forhistological diagnosis or diagnostic procedures would be used forresearch; and histopathologic assessment indicating adequate amounts oftumor tissue for inclusion in this research study.

Exclusion Criteria:

Distant metastases; no tumor block available from initial core biopsy orfrom the surgical resection; or no tumor or very little tumor (<5% ofthe overall tissue on the slide) in block as assessed by examination ofthe H&E slide by the Pathologist.

Study Design

Eighty-nine evaluable patients (from a set of 96 clinically evaluablepatients) were identified and studied. The levels of 384 mRNA specieswere measured by RT-PCR, representing products of candidatecancer-related genes that were selected from the biomedical researchliterature. Only one gene was lost due to inadequate signal.

Patient characteristics were as follows: Mean age: 50 years; Tumorgrades: 24% Well, 55% Moderate, and 21% Poor; Sixty-three % of patientswere ER positive {by immunohistochemistry}; Seventy % of patients hadpositive lymph nodes.

All patients were given primary neoadjuvant chemotherapy: Doxorubicinplus Taxol 3weeks/3 cycles followed by Taxol® (paclitaxel) 1 week/12cycles. Surgical removal of the tumor followed completion ofchemotherapy. Core tumor biopsy specimens were taken prior to start ofchemotherapy, and served as the source of RNA for the RT-PCR assay.

Materials and Methods

Fixed paraffin-embedded (FPE) tumor tissue from biopsy was obtainedprior to and after chemotherapy. Core biopsies were taken prior tochemotherapy. In that case, the pathologist selected the mostrepresentative primary tumor block, and submitted nine 10 micronsections for RNA analysis. Specifically, a total of 9 sections (10microns in thickness each) were prepared and placed in three CostarBrand Microcentrifuge Tubes (Polypropylene, 1.7 mL tubes, clear; 3sections in each tube) and pooled.

Messenger RNA was extracted using the MasterPure™ RNA Purification Kit(Epicentre Technologies) and quantified by the RiboGreen® fluorescencemethod (Molecular probes). Molecular assays of quantitative geneexpression were performed by RT-PCR, using the ABI PRISM 7900™ SequenceDetection System™ (Perkin-Elmer-Applied Biosystems, Foster City, Calif.,USA). ABI PRISM 7900™ consists of a thermocycler, laser, charge-coupleddevice (CCD), camera and computer. The system amplifies samples in a384-well format on a thermocycler. During amplification, laser-inducedfluorescent signal is collected in real-time for all 384 wells, anddetected at the CCD. The system includes software for running theinstrument and for analyzing the data.

Analysis and Results

Tumor tissue was analyzed for 384 genes. The threshold cycle (CT) valuesfor each patient were normalized based on the median of a subset of thescreened genes for that particular patient, selected based on lack ofcorrelation with clinical outcome (central normalization strategy).Patient beneficial response to chemotherapy was defined as pathologiccomplete response (pCR). Patients were formally assessed for response atthe completion of all chemotherapy.

A clinical complete response (cCR) requires complete disappearance ofall clinically detectable disease, either by physical examination ordiagnostic breast imaging.

A pathologic complete response (pCR) requires absence of residual breastcancer on histologic examination of biopsied breast tissue, lumpectomyor mastectomy specimens following primary chemotherapy. Residual ductalcarcinoma in situ (DCIS) may be present. Residual cancer in regionalnodes may not be present. Of the 89 evaluable patients 11 (12%) had apathologic complete response (pCR). Seven of these patients were ERnegative.

A partial clinical response was defined as a ≧50% decrease in tumor area(sum of the products of the longest perpendicular diameters) or a ≧50%decrease in the sum of the products of the longest perpendiculardiameters of multiple lesions in the breast and axilla. No area ofdisease may increase by >25% and no new lesions may appear.

Analysis was performed by comparing the relationship between normalizedgene expression and the binary outcomes of pCR or no pCR. Univariategeneralized models were used with probit or logit link functions. See,e.g. Van K. Borooah, LOGIT and PROBIT, Ordered Multinominal Models, SageUniversity Paper, 2002.

Table 1 presents pathologic response correlations with gene expression,and lists the 86 genes for which the p-value for the differences betweenthe groups was <0.1. The second column (with the heading “Direction”)denotes whether increased expression correlates with decreasing orincreasing likelihood of response to chemotherapy. The statisticalsignificance of the predictive value for each gene is given by P-value(right hand column)

Probit Link Gene Direction Intercept Slope P-value TBP Decreasing 0.05752.4354 0.0000 ILT.2 Increasing 0.5273 −0.9489 0.0003 ABCC5 Decreasing0.9872 0.8181 0.0003 CD18 Increasing 3.4735 −1.0787 0.0007 GATA3Decreasing 0.6175 0.2975 0.0008 DICER1 Decreasing −0.9149 1.4875 0.0013MSH3 Decreasing 2.6875 0.9270 0.0013 GBP1 Increasing 1.7649 −0.54100.0014 IRS1 Decreasing 1.3576 0.5214 0.0016 CD3z Increasing 0.1567−0.5162 0.0018 Fasl Increasing −0.6351 −0.4050 0.0019 TUBB Decreasing1.2745 0.8267 0.0025 BAD Decreasing 0.9993 1.1325 0.0033 ERCC1Decreasing 0.0327 1.0784 0.0039 MCM6 Increasing 0.1371 −0.8008 0.0052 PRDecreasing 1.6079 0.1764 0.0054 APC Decreasing 0.7264 1.0972 0.0061GGPS1 Decreasing 1.0906 0.8124 0.0062 KRT18 Decreasing −0.8029 0.45060.0063 ESRRG Decreasing 2.0198 0.2262 0.0063 E2F1 Increasing 0.2188−0.5277 0.0068 AKT2 Decreasing −1.3566 1.1902 0.0074 A. CateninDecreasing −0.6859 0.9279 0.0079 CEGP1 Decreasing 1.3355 0.1875 0.0091NPD009 Decreasing 1.3996 0.2971 0.0092 MAPK14 Decreasing 2.6253 1.60070.0093 RUNX1 Decreasing −0.4138 0.7214 0.0103 ID2 Increasing 1.7326−0.7032 0.0104 G. Catenin Decreasing −0.1221 0.5954 0.0110 FBXO5Increasing 0.3421 −0.4935 0.0110 FHIT Decreasing 1.9966 0.4989 0.0113MTA1 Decreasing 0.3127 0.6069 0.0133 ERBB4 Decreasing 1.4591 0.14360.0135 FUS Decreasing −0.6150 0.9415 0.0137 BBC3 Decreasing 2.47960.6495 0.0138 IGE1R Decreasing 1.1998 0.3116 0.0147 CD9 Decreasing−0.9292 0.5747 0.0156 TP53BP1 Decreasing 1.4325 0.8122 0.0169 MUC1Decreasing 0.8881 0.2140 0.0175 IGFBP5 Decreasing −0.6180 0.4880 0.0181rhoC Decreasing −0.1726 0.6860 0.0184 RALBP1 Decreasing 0.2383 0.95090.0185 CDC20 Increasing 1.3204 −0.4390 0.0186 STAT3 Decreasing −0.97630.7023 0.0194 ERK1 Decreasing 0.8577 0.6496 0.0198 HLA.DPB1 Increasing3.6300 −0.6035 0.0202 SGCB Decreasing 0.6171 0.7823 0.0208 CGAIncreasing 0.0168 −0.1450 0.0209 DHPS Decreasing 0.2957 0.7840 0.0216MGMT Decreasing 0.9238 0.6876 0.0226 CRIP2 Decreasing 0.5524 0.43940.0230 MMP12 Increasing 0.4208 −0.2419 0.0231 ErbB3 Decreasing 0.94380.2798 0.0233 RAP1GDS1 Decreasing 0.2617 0.7672 0.0235 CDC25B Increasing1.6965 −0.5356 0.0264 IL6 Increasing 0.0592 −0.2388 0.0272 CCND1Decreasing 0.2260 0.2992 0.0272 CYBA Increasing 2.6493 −0.5175 0.0287PRKCD Decreasing 0.2125 0.6745 0.0291 DR4 Increasing 0.3039 −0.53210.0316 Hepsin Decreasing 1.9211 0.1873 0.0318 CRABP1 Increasing 1.0309−0.1287 0.0320 AK055699 Decreasing 2.0442 0.1765 0.0343 Contig.51037Increasing 0.7857 −0.1131 0.0346 VCAM1 Increasing 1.1866 −0.3560 0.0346FYN Increasing 1.5502 −0.5624 0.0359 GRB7 Increasing 1.3592 −0.16460.0375 AKAP.2 Increasing 1.7946 −0.7008 0.0382 RASSF1 Increasing 1.1972−0.0390 0.0384 MCP1 Increasing 1.3700 −0.3805 0.0388 ZNF38 Decreasing1.7957 0.4993 0.0395 MCM2 Increasing 1.0574 −0.4695 0.0426 GBP2Increasing 1.4095 −0.4559 0.0439 SEMA3F Decreasing 1.2706 0.3725 0.0455CD31 Increasing 1.9913 −0.5955 0.0459 COL1A1 Decreasing −1.9861 0.38120.0466 ER2 Increasing −0.5204 −0.2617 0.0471 BAG1 Decreasing 0.67310.5070 0.0472 AKT1 Decreasing −0.4467 0.5768 0.0480 COL1A2 Decreasing−1.0233 0.3804 0.0490 STAT1 Increasing 1.9447 −0.4062 0.0498 Wnt.5aDecreasing 2.2244 0.2983 0.0518 PTPD1 Decreasing 1.2950 0.4834 0.0552RAB6C Decreasing 0.4841 0.5635 0.0717 TK1 Increasing 0.6127 −0.36250.0886 Bcl2 Decreasing 1.1459 0.2509 0.0959

Based on the data set forth in Table 1, increased expression of thefollowing genes correlates with increased likelihood of completepathologic response to treatment: ILT.2; CD18; GBP1; CD3z; fas1; MCM6;E2F1; ID2; FBXO5; CDC20; HLA.DPB1; CGA; MMP12; CDC25B; IL6; CYBA; DR4;CRABP1; Contig.51037; VCAM1; FYN; GRB7; AKAP.2; RASSF1; MCP1; MCM2;GBP2; CD31; ER2; STAT1; TK1; while increased expression of the followinggenes correlates with decreased likelihood of complete pathologicresponse to treatment: TBP; ABCC5; GATA3; DICER1; MSH3; IRS1; TUBB; BAD;ERCC1; PR; APC; GGPS1; KRT18; ESRRG; AKT2; A.Catenin; CEGP1; NPD009;MAPK14; RUNX1; G.Catenin; FHIT; MTA1; ErbB4; FUS; BBC3; IGF1R; CD9;TP53BP1; MUC1; IGFBP5; rhoC; RALBP1; STAT3; ERK1; SGCB; DHPS; MGMT;CRIP2; ErbB3; RAP1GDS1; CCND1; PRKCD; Hepsin; AK055699; ZNF38; SEMA3F;COL1A1; BAG1; AKT1; COL1A2; Wnt.5a; PTPD1; RAB6C; Bcl2.

The relationship between the recurrence risk algorithm (described incopending U.S. application Ser. No. 60/486,302) and pCR was alsoinvestigated. The algorithm incorporates the measured levels of 21 mRNAspecies. Sixteen mRNAs (named below) were candidate clinical markers andthe remaining 5 (ACTB, GAPD, GUSB, RPLPO, and TFRC) were referencegenes. Reference-normalized expression measurements range from 0 to 15,where a one unit increase reflects a 2-fold increase in RNA.

The Recurrence Score (RS) is calculated from the quantitative expressionof four sets of marker genes (an estrogen receptor group of 4genes—ESR1, PGR, BCL2, and SCUBE2; a proliferation set of 5 genes—MKI67,MYBL2, BIRC5, CCNB1, and STK6; a HER2 set of 2 genes—ERBB2 and GRB7, aninvasion group of 2 genes—MMP11 and CTSL2) and 3 other individualgenes—GSTM1, BAG1, and CD68.

Although the genes and the multiplication factors used in the equationmay vary, in a typical embodiment, the following equation may be used tocalculate RS:

RS(range, 0 − 100) = +0.47 × HER2  Group  Score − 0.34 × ER  Group  Score + 1.04 × Proliferation  Group  Score + 0.10 × Invasion  Group  Score + 0.05 × CD68 − 0.08 × GSTM1 − 0.07 × BAG1

Application of this algorithm to study clinical and gene expression datasets yields a continuous curve relating RS to pCR values, as shown inFIG. 1. Examination of these data shows that patients with RS>50 are inthe upper 50 percentile of patients in terms of likelihood of responseto chemotherapy, and that patients with RS<35 are in the lower 50percentile of patients in terms of likelihood of response tochemotherapy.

All references cited throughout the disclosure are hereby expresslyincorporated by reference.

While the invention has been described with emphasis upon certainspecific embodiments, it is be apparent to those skilled in the art thatvariations and modification in the specific methods and techniques arepossible. Accordingly, this invention includes all modificationsencompassed within the spirit and scope of the invention as defined bythe following claims.

TABLE 2 A-Catenin NM_001903 S2138/A-Cate.f2 CGTTCCGATCCTCTATACTGCAT 23SEQ ID NO: 1 A-Catenin NM_001903 S2139/A-Cate.r2 AGGTCCCTGTTGGCCTTATAGG22 SEQ ID NO: 2 A-Catenin NM_001903 S4725/A-Cate.p2ATGCCTACAGCACCCTGATGTCGCA 25 SEQ ID NO: 3 ABCC5 NM_005688 S5605/ABCC5.f1TGCAGACTGTACCATGCTGA 20 SEQ ID NO: 4 ABCC5 NM_005688 S5606/ABCC5.r1GGCCAGCACCATAATCCTAT 20 SEQ ID NO: 5 ABCC5 NM_005688 S5607/ABCC5.p1CTGCACACGGTTCTAGGCTCCG 22 SEQ ID NO: 6 AK055699 AK055699 S2097/AK0556.f1CTGCATGTGATTGAATAAGAAACAAGA 27 SEQ ID NO: 7 AK055699 AK055699S2098/AK0556.r1 TGTGGACCTGATCCCTGTACAC 22 SEQ ID NO: 8 AK055699 AK055699S5057/AK0556.p1 TGACCACACCAAAGCCTCCCTGG 23 SEQ ID NO: 9 AKAP-2 NM_007203S1374/AKAP-2.f1 ACGAATTGTCGGTGAGGTCT 20 SEQ ID NO: 10 AKAP-2 NM_007203S1375/AKAP-2.r1 GTCCATGCTGAAATCATTGG 20 SEQ ID NO: 11 AKAP-2 NM_007203S4934/AKAP-2.p1 CAGGATACCACAGTCCTGGAGACCC 25 SEQ ID NO: 12 AKT1NM_005163 S0010/AKT1.f3 CGCTTCTATGGCGCTGAGAT 20 SEQ ID NO: 13 AKT1NM_005163 S0012/AKT1.r3 TCCCGGTACACCACGTTCTT 20 SEQ ID NO: 14 AKT1NM_005163 S4776/AKT1.p3 CAGCCCTGGACTACCTGCACTCGG 24 SEQ ID NO: 15 AKT2NM_001626 S0828/AKT2.f3 TCCTGCCACCCTTCAAACC 19 SEQ ID NO: 16 AKT2NM_001626 S0829/AKT2.r3 GGCGGTAAATTCATCATCGAA 21 SEQ ID NO: 17 AKT2NM_001626 S4727/AKT2.p3 CAGGTCACGTCCGAGGTCGACACA 24 SEQ ID NO: 18 APCNM_000038 S0022/APC.f4 GGACAGCAGGAATGTGTTTC 20 SEQ ID NO: 19 APCNM_000038 S0024/APC.r4 ACCCACTCGATTTGTTTCTG 20 SEQ ID NO: 20 APCNM_000038 S4888/APC.p4 CATTGGCTCCCCGTGACCTGTA 22 SEQ ID NO: 21 BADNM_032989 S2011/BAD.f1 GGGTCAGGTGCCTCGAGAT 19 SEQ ID NO: 22 BADNM_032989 S2012/BAD.r1 CTGCTCACTCGGCTCAAACTC 21 SEQ ID NO: 23 BADNM_032989 S5058/BAD.p1 TGGGCCCAGAGCATGTTCCAGATC 24 SEQ ID NO: 24 BAG1NM_004323 S1386/BAG1.f2 CGTTGTCAGCACTTGGAATACAA 23 SEQ ID NO: 25 BAG1NM_004323 S1387/BAG1.r2 GTTCAACCTCTTCCTGTGGACTGT 24 SEQ ID NO: 26 BAG1NM_004323 S4731/BAG1.p2 CCCAATTAACATGACCCGGCAACCAT 26 SEQ ID NO: 27 BBC3NM_014417 S1584/BBC3.f2 CCTGGAGGGTCCTGTACAAT 20 SEQ ID NO: 28 BBC3NM_014417 S1585/BBC3.r2 CTAATTGGGCTCCATCTCG 19 SEQ ID NO: 29 BBC3NM_014417 S4890/BBC3.p2 CATCATGGGACTCCTGCCCTTACC 24 SEQ ID NO: 30 BcI2NM_000633 S0043/BcI2.f2 CAGATGGACCTAGTACCCACTGAGA 25 SEQ ID NO: 31 BcI2NM_000633 S0045/BcI2.r2 CCTATGATTTAAGGGCATTTTTCC 24 SEQ ID NO: 32 BcI2NM_000633 S4732/BcI2.p2 TTCCACGCCGAAGGACAGCGAT 22 SEQ ID NO: 33 CCND1NM_001758 S0058/CCND1.f3 GCATGTTCGTGGCCTCTAAGA 21 SEQ ID NO: 34 CCND1NM_001758 S0060/CCND1.r3 CGGTGTAGATGCACAGCTTCTC 22 SEQ ID NO: 35 CCND1NM_001758 S4986/CCND1.p3 AAGGAGACCATCCCCCTGACGGC 23 SEQ ID NO: 36 CD18NM_000211 S0061/CD18.f2 CGTCAGGACCCACCATGTCT 20 SEQ ID NO: 37 CD18NM_000211 S0063/CD18.r2 GGTTAATTGGTGACATCCTCAAGA 24 SEQ ID NO: 38 CD18NM_000211 S4987/CD18.p2 CGCGGCCGAGACATGGCTTG 20 SEQ ID NO: 39 CD31NM_000442 S1407/CD31.f3 TGTATTTCAAGACCTCTGTGCACTT 25 SEQ ID NO: 40 CD31NM_000442 S1408/CD31.r3 TTAGCCTGAGGAATTGCTGTGTT 23 SEQ ID NO: 41 CD31NM_000442 S4939/CD31.p3 TTTATGAACCTGCCCTGCTCCCACA 25 SEQ ID NO: 42 CD3zNM_000734 S0064/CD3z.f1 AGATGAAGTGGAAGGCGCTT 20 SEQ ID NO: 43 CD3zNM_000734 S0066/CD3z.r1 TGCCTCTGTAATCGGCAACTG 21 SEQ ID NO: 44 CD3zNM_000734 S4988/CD3z.p1 CACCGCGGCCATCCTGCA 18 SEQ ID NO: 45 CD9NM_001769 S0686/CD9.f1 GGGCGTGGAACAGTTTATCT 20 SEQ ID NO: 46 CD9NM_001769 S0687/CD9.r1 CACGGTGAAGGTTTCGAGT 19 SEQ ID NO: 47 CD9NM_001769 S4792/CD9.p1 AGACATCTGCCCCAAGAAGGACGT 24 SEQ ID NO: 48 CDC20NM_001255 S4447/CDC20.f1 TGGATTGGAGTTCTGGGAATG 21 SEQ ID NO: 49 CDC20NM_001255 S4448/CDC20.r1 GCTTGCACTCCACAGGTACACA 22 SEQ ID NO: 50 CDC20NM_001255 S4449/CDC20.p1 ACTGGCCGTGGCACTGGACAACA 23 SEQ ID NO: 51 CDC25BNM_O21874 S1160/CDC25B.f1 AAACGAGCAGTTTGCCATCAG 21 SEQ ID NO: 52 CDC25BNM_021874 S1161/CDC25B.r1 GTTGGTGATGTTCCGAAGCA 20 SEQ ID NO: 53 CDC25BNM_021874 S4842/CDC25B.p1  CCTCACCGGCATAGACTGGAAGCG 24 SEQ ID NO: 54CEGP1 NM_020974 S1494/CEGP1.f2 TGACAATCAGCACACCTGCAT 21 SEQ ID NO: 55CEGP1 NM_020974 S1495/CEGP1.r2 TGTGACTACAGCCGTGATCCTTA 23 SEQ ID NO: 56CEGP1 NM_020974 S4735/CEGP1.p2 CAGGCCCTCTTCCGAGCGGT 20 SEQ ID NO: 57CGA(CHGA NM_001275 S3221/CGA (C.f3  CTGAAGGAGCTCCAAGACCT 20SEQ ID NO: 58 CGA(CHGA NM_001275 S3222/CGA (C.r3  CAAAACCGCTGTGTTTCTTC20 SEQ ID NO: 59 CGA(CHGA NM_001275 S3254/CGA (C.p3 TGCTGATGTGCCCTCTCCTTGG 22 SEQ ID NO: 60 COL1A1 NM_000088S4531/COL1A1.f1  GTGGCCATCCAGCTGACC 18 SEQ ID NO: 61 COL1A1 NM_000088S4532/COL1A1.r1  CAGTGGTAGGTGATGTTCTGGGA 23 SEQ ID NO: 62 COL1A1NM_000088 S4533/COL1A1.p1  TCCTGCGCCTGATGTCCACCG 21 SEQ ID NO: 63 COL1A2NM_000089 S4534/COL1A2.f1  CAGCCAAGAACTGGTATAGGAGCT 24 SEQ ID NO: 64COL1A2 NM_O00089 S4535/COL1A2.r1  AAACTGGCTGCCAGCATTG 19 SEQ ID NO: 65COL1A2 NM_000089 S4536/COL1A2.p1  TCTCCTAGCCAGACGTGTTTCTTGTCCTTG 30SEQ ID NO: 66 Contig 5103:    XM_058945 S2070/Contig.f1CGACAGTTGCGATGAAAGTTCTAA 24 SEQ ID NO: 67 Contig 5103: XM_058945S2071/Contig.r1 GGCTGCTAGAGACCATGGACAT 22 SEQ ID NO: 68 Contig 5103: XM_058945 S5059/Contig.pl CCTCCTCCTGTTGCTGCCACTAATGCT 27 SEQ ID NO: 69CRABP1 NM_004378 S5441/CRABP1.f3  AACTTCAAGGTCGGAGAAGG 20 SEQ ID NO: 70CRABP1 NM_004378 S5442/CRABP1.r3  TGGCTAAACTCCTGCACTTG 20 SEQ ID NO: 71CRABP1 NM_004378 S5443/CRABP1.p3  CCGTCCACGGTCTCCTCCTCA 21 SEQ ID NO: 72CRIP2 NM_001312 S5676/CRIP2.f3 GTGCTACGCCACCCTGTT 18 SEQ ID NO: 73 CRIP2NM_001312 S5677/CRIP2.r3 CAGGGGCTTCTCGTAGATGT 20 SEQ ID NO: 74 CRIP2NM_001312 S5678/CRIP2.p3 CCGATGTTCACGCCTTTGGGTC 22 SEQ ID NO: 75 CYBANM_000101 S5300/CYBA.f1 GGTGCCTACTCCATTGTGG 19 SEQ ID NO: 76 CYBANM_000101 S5301/CYBA.r1 GTGGAGCCCTTCTTCCTCTT 20 SEQ ID NO: 77 CYBANM_000101 S53021CYBA.p1 TACTCCAGCAGGCACACAAACACG 24 SEQ ID NO: 78 DHPSNM_013407 S4519/DHPS.f3 GGGAGAACGGGATCAATAGGAT 22 SEQ ID NO: 79 DHPSNM_013407 S4520/DHPS.r3 GCATCAGCCAGTCCTCAAACT 21 SEQ ID NO: 80 DHPSNM_013407 S4521/DHPS.p3 CTCATTGGGCACCAGCAGGTTTCC 24 SEQ ID NO: 81 DICER1NM_177438 S5294/DICER1.f2  TCCAATTCCAGCATCACTGT 20 SEQ ID NO: 82 DICER1NM_177438 S5295/DICER1.r2  GGCAGTGAAGGCGATAAAGT 20 SEQ ID NO: 83 DICER1NM_177438 S5296/DICER1.p2  AGAAAAGCTGTTTGTCTCCCCAGCA 25 SEQ ID NO: 84DR4 NM_003844 S2532/DR4.f2 TGCACAGAGGGTGTGGGTTAC 21 SEQ ID NO: 85 DR4NM_003844 S2533/DR4.r2 TCTTCATCTGATTTACAAGCTGTACATG 28 SEQ ID NO: 86 DR4NM_003844 S4981/DR4.p2 CAATG CTICCAACAATTTGTTTGCTTGCC 29 SEQ ID NO: 87E2F1 NM_005225 S3063/E2F1.f3 ACTCCCTCTACCCTTGAGCA 20 SEQ ID NO: 88 E2F1NM_005225 S3064/E2F1.r3 CAGGCCTCAGTTCCTTCAGT 20 SEQ ID NO: 89 E2F1NM_005225 S4821/E2F1.p3 CAGAAGAACAGCTCAGGGACCCCT 24 SEQ ID NO: 90 ER2NM_001437 S0109/ER2.f2 TGGTCCATCGCCAGTTATCA 20 SEQ ID NO: 91 ER2NM_001437 S0111/ER2.r2 TGTTCTAGCGATCTTGCTTCACA 23 SEQ ID NO: 92 ER2NM_001437 S5001/ER2.p2 ATCTGTATGCGGAACCTCAAAAGAGTCCCT 30 SEQ ID NO: 93ErbB3 NM_001982 S0112/ErbB3.f1 CGGTTATGTCATGCCAGATACAC 23 SEQ ID NO: 94ErbB3 NM_001982 S0114/ErbB3.r1 GAACTGAGACCCACTGAAGAAAGG 24 SEQ ID NO: 95ErbB3 NM_001982 S5002/ErbB3.p1 CCTCAAAGGTACTCCCTCCTCCCGG 25SEQ ID NO: 96 ERBB4 NM_005235 S1231/ERBB4.f3 TGGCTCTTAATCAGTTTCGTTACCT25 SEQ ID NO: 97 ERBB4 NM_005235 S1232/ERBB4.r3CAAGGCATATCGATCCTCATAAAGT 25 SEQ ID NO: 98 ERBB4 NM_005235S4891/ERBB4.p3 TGTCCCACGAATAATGCGTAAATTCTCCAG 30 SEQ ID NO: 99 ERCC1NM_001983 S2437/ERCC1.f2 GTCCAGGTGGATGTGAAAGA 20 SEQ ID NO: 100 ERCC1NM_001983 S2438/ERCC1.r2 CGGCCAGGATACACATCTTA 20 SEQ ID NO: 101 ERCC1NM_001983 S4920/ERCC1.p2 CAGCAGGCCCTCAAGGAGCTG 21 SEQ ID NO: 102 ERK1Z11696 S1560/ERK1.f3 ACGGATCACAGTGGAGGAAG 20 SEQ ID NO: 103 ERK1 Z11696S1561/ERK1.r3 CTCATCCGTCGGGTCATAGT 20 SEQ ID NO: 104 ERK1 Z11696S4882/ERK1.p3 CGCTGGCTCACCCCTACCTG 20 SEQ ID NO: 105 ESRRG NM_001438S6130/ESRRG.f3 CCAGCACCATTGTTGAAGAT 20 SEQ ID NO: 106 ESRRG NM_001438S6131/ESRRG.r3 AGTCTCTTGGGCATCGAGTT 20 SEQ ID NO: 107 ESRRG NM_001438S6132/ESRRG.p3 CCCCAGACCAAGTGTGAATACATGCT 26 SEQ ID NO: 108 faslNM_000639 S0121/fasl.f2 GCACTTTGGGATTCTTTCCATTAT 24 SEQ ID NO: 109 faslNM_000639 S0123/fasl.r2 GCATGTAAGAAGACCCTCACTGAA 24 SEQ ID NO: 110 faslNM_000639 S5004/fasl.p2 ACAACATTCTCGGTGCCTGTAACAAAGAA 29 SEQ ID NO: 111FBXO5 NM_012177 S2017/FBXO5.r1 GGATTGTAGACTGTCACCGAAATTC 25SEQ ID NO: 112 FBXO5 NM_012177 S2018/FBXO5.f1GGCTATTCCTCATTTTCTCTACAAAGTG 28 SEQ ID NO: 113 FBXO5 NM_012177S5061/FBXO5.p1 CCTCCAGGAGGCTACCTTCTTCATGTTCAC 30 SEQ ID NO: 114 FHITNM_002012 S2443/FHIT.f1 CCAGTGGAGCGCTTCCAT 18 SEQ ID NO: 115 FHITNM_002012 S2444/FHIT.r1 CTCTCTGGGTCGTCTGAAACAA 22 SEQ ID NO: 116 FHITNM_002012 S4921/FHIT.p1 TCGGCCACTTCATCAGGACGCAG 23 SEQ ID NO: 117 FUSNM_004960 S2936/FUS.f1 GGATAATTCAGACAACAACACCATCT 26 SEQ ID NO: 118 FUSNM_004960 S2937/FUS.r1 TGAAGTAATCAGCCACAGACTCAAT 25 SEQ ID NO: 119 FUSNM_004960 S4801/FUS.p1 TCAATTGTAACATTCTCACCCAGGCCTTG 29 SEQ ID NO: 120FYN NM_002037 S5695/FYN.f3 GAAGCGCAGATCATGAAGAA 20 SEQ ID NO: 121 FYNNM_002037 S5696/FYN.r3 CTCCTCAGACACCACTGCAT 20 SEQ ID NO: 122 FYNNM_002037 S5697/FYN.p3 CTGAAGCACGACAAGCTGGTCCAG 24 SEQ ID NO: 123G-Catenin NM_002230 S2153/G-Cate.f1 TCAGCAGCAAGGGCATCAT 19SEQ ID NO: 124 G-Catenin NM_002230 S2154/G-Cate.r1GGTGGTTTTCTTGAGCGTGTACT 23 SEQ ID NO: 125 G-Catenin NM_002230S5044/G-Cate.p1 CGCCCGCAGGCCTCATCCT 19 SEQ ID NO: 126 GATA3 NM_002051S0127/GATA3.f3 CAAAGGAGCTCACTGTGGTGTCT 23 SEQ ID NO: 127 GATA3 NM_002051S0129/GATA3.r3 GAGTCAGAATGGCTTATTCACAGATG 26 SEQ ID NO: 128 GATA3NM_002051 S5005/GATA3.p3 TGTTCCAACCACTGAATCTGGACC 24 SEQ ID NO: 129 GBP1NM_002053 S5698/GBP1.f1 TTGGGAAATATTTGGGCATT 20 SEQ ID NO: 130 GBP1NM_002053 S5699/GBP1.r1 AGAAGCTAGGGTGGTTGTCC 20 SEQ ID NO: 131 GBP1NM_002053 S5700/GBP1.p1 TTGGGACATTGTAGACTTGGCCAGAC 26 SEQ ID NO: 132GBP2 NM_004120 S5707/GBP2.f2 GCATGGGAACCATCAACCA 19 SEQ ID NO: 133 GBP2NM_004120 S5708/GBP2.r2 TGAGGAGTTTGCCTTGATTCG 21 SEQ ID NO: 134 GBP2NM_004120 S5709/GBP2.p2 CCATGGACCAACTTCACTATGTGACAGAGC 30 SEQ ID NO: 135GGPS1 NM_004837 S1590/GGPS1.f1 CTCCGACGTGGCTTTCCA 18 SEQ ID NO: 136GGPS1 NM_004837 S1591/GGPS1.r1 CGTAATTGGCAGAATTGATGACA 23 SEQ ID NO: 137GGPS1 NM_004837 S4896/GGPS1.p1 TGGCCCACAGCATCTATGGAATCCC 25SEQ ID NO: 138 GRB7 NM_005310 S0130/GRB7.f2 CCATCTGCATCCATCTTGTT 20SEQ ID NO: 139 GRB7 NM_005310 S0132/GRB7.r2 GGCCACCAGGGTATTATCTG 20SEQ ID NO: 140 GRB7 NM_005310 S4726/GRB7.p2 CTCCCCACCCTTGAGAAGTGCCT 23SEQ ID NO: 141 Hepsin NM_002151 S2269/Hepsin.f1 AGGCTGCTGGAGGTCATCTC 20SEQ ID NO: 142 Hepsin NM_002151 S2270/Hepsin.r1 CTTCCTGCGGCCACAGTCT 19SEQ ID NO: 143 Hepsin NM_002151 S2271/Hepsin.p1 CCAGAGGCCGTTTCTTGGCCG 21SEQ ID NO: 144 HLA-DPB1 NM_O02121 S4573/HLA-DP.f1 TCCATGATGGTTCTGCAGGTT21 SEQ ID NO: 145 HLA-DPB1 NM_002121 S4574/HLA-DP.r1TGAGCAGCACCATCAGTAACG 21 SEQ ID NO: 146 HLA-DPB1 NM_002121S4575/HLA-DP.p1 CCCCGGACAGTGGCTCTGACG 21 SEQ ID NO: 147 ID2 NM_002166S0151/ID2.f4 AACGACTGCTACTCCAAGCTCAA 23 SEQ ID NO: 148 ID2 NM_002166S0153/ID2.r4 GGATTTCCATCTTGCTCACCTT 22 SEQ ID NO: 149 ID2 NM_002166S5009/ID2.p4 TGCCCAGCATCCCCCAGAACAA 22 SEQ ID NO: 150 IGF1R NM_000875S1249/IGF1R.f3 GCATGGTAGCCGAAGATTTCA 21 SEQ ID NO: 151 IGF1R NM_000875S1250/IGF1R.r3 TTTCCGGTAATAGTCTGTCTCATAGATATC 30 SEQ ID NO: 152 IGF1RNM_000875 S4895/IGF1R.p3 CGCGTCATACCAAAATCTCCGATTTTGA 28 SEQ ID NO: 153IL6 NM_000600 S0760/IL6.f3 CCTGAACCTTCCAAAGATGG 20 SEQ ID NO: 154 IL6NM_000600 S0761/1L6.r3 ACCAGGCAAGTCTCCTCATT 20 SEQ ID NO: 155 IL6NM_000600 S4800/IL6.p3 CCAGATTGGAAGCATCCATCTTTTTCA 27 SEQ ID NO: 156ILT-2 NM_006669 S1611/ILT-2.f2 AGCCATCACTCTCAGTGCAG 20 SEQ ID NO: 157ILT-2 NM_006669 S1612/ILT-2.r2 ACTGCAGAGTCAGGGTCTCC 20 SEQ ID NO: 158ILT-2 NM_006669 S4904/ILT-2.p2 CAGGTCCTATCGTGGCCCCTGA 22 SEQ ID NO: 159IRS1 NM_005544 S1943/IRS1.f3 CCACAGCTCACCTTCTGTCA 20 SEQ ID NO: 160 IRS1NM_005544 S1944/IRS1.r3 CCTCAGTGCCAGTCTCTTCC 20 SEQ ID NO: 161 IRS1NM_005544 S5050/IRS1.p3 TCCATCCCAGCTCCAGCCAG 20 SEQ ID NO: 162 KRT18NM_000224 S1710/KRT18.f2 AGAGATCGAGGCTCTCAAGG 20 SEQ ID NO: 163 KRT18NM_000224 S1711/KRT18.r2 GGCCTTTTACTTCCTCTTCG 20 SEQ ID NO: 164 KRT18NM_000224 S4762/KRT18.p2 TGGTTCTTCTTCATGAAGAGCAGCTCC 27 SEQ ID NO: 165MAPK14 NM_139012 S5557/MAPK14.f2 TGAGTGGAAAAGCCTGACCTATG 23SEQ ID NO: 166 MAPK14 NM_139012 S5558/MAPK14.r2 GGACTCCATCTCTTCTTGGTCAA23 SEQ ID NO: 167 MAPK14 NM_139012 S5559/MAPK14.p2TGAAGTCATCAGCTTTGTGCCACCACC 27 SEQ ID NO: 168 MCM2 NM_004526S1602/MCM2.f2 GACTTTTGCCCGCTACCTTTC 21 SEQ ID NO: 169 MCM2 NM_004526S1603/MCM2.r2 GCCACTAACTGCTTCAGTATGAAGAG 26 SEQ ID NO: 170 MCM2NM_004526 S4900/MCM2.p2 ACAGCTCATTGTTGTCACGCCGGA 24 SEQ ID NO: 171 MCM6NM_005915 S1704/MCM6.f3 TGATGGTCCTATGTGTCACATTCA 24 SEQ ID NO: 172 MCM6NM_005915 S1705/MCM6.r3 TGGGACAGGAAACACACCAA 20 SEQ ID NO: 173 MCM6NM_005915 S4919/MCM6.p3 CAGGTTTCATACCAACACAGGCTTCAGCAC 30 SEQ ID NO: 174MCP1 NM_002982 S1955/MCP1.f1 CGCTCAGCCAGATGCAATC 19 SEQ ID NO: 175 MCP1NM_002982 S1956/MCP1.r1 GCACTGAGATCTTCCTATTGGTGAA 25 SEQ ID NO: 176 MCP1NM_002982 S5052/MCP1.p1 TGCCCCAGTCACCTGCTGTTA 21 SEQ ID NO: 177 MGMTNM_002412 S1922/MGMT.f1 GTGAAATGAAACGCACCACA 20 SEQ ID NO: 178 MGMTNM_002412 S1923/MGMT.r1 GACCCTGCTCACAACCAGAC 20 SEQ ID NO: 179 MGMTNM_002412 S5045/MGMT.p1 CAGCCCTTTGGGGAAGCTGG 20 SEQ ID NO: 180 MMP12NM_002426 S4381/MMP12.f2 CCAACGCTTGCCAAATCCT 19 SEQ ID NO: 181 MMP12NM_002426 S4382/MMP12.r2 ACGGTAGTGACAGCATCAAAACTC 24 SEQ ID NO: 182MMP12 NM_002426 S4383/MMP12.p2 AACCAGCTCTCTGTGACCCCAATT 24SEQ ID NO: 183 MSH3 NM_002439 S5940/MSH3.f2 TGATTACCATCATGGCTCAGA 21SEQ ID NO: 184 MSH3 NM_002439 S5941/MSH3.r2 CTTGTGAAAATGCCATCCAC 20SEQ ID NO: 185 MSH3 NM_002439 S5942/MSH3.p2 TCCCAATTGTCGCTTCTTCTGCAG 24SEQ ID NO: 186 MTA1 NM_004689 S2369/MTA1.f1 CCGCCCTCACCTGAAGAGA 19SEQ ID NO: 187 MTA1 NM_004689 S2370/MTA1.r1 GGAATAAGTTAGCCGCGCTTCT 22SEQ ID NO: 188 MTA1 NM_004689 S4855/MTA1.p1 CCCAGTGTCCGCCAAGGAGCG 21SEQ ID NO: 189 MUC1 NM_002456 S0782/MUC1.f2 GGCCAGGATCTGTGGTGGTA 20SEQ ID NO: 190 MUC1 NM_002456 S0783/MUC1.r2 CTCCACGTCGTGGACATTGA 20SEQ ID NO: 191 MUC1 NM_002456 S4807/MUC1.p2 CTCTGGCCTTCCGAGAAGGTACC 23SEQ ID NO: 192 NPD009 (AB  NM_020686 S4474/NPD009.f3GGCTGTGGCTGAGGCTGTAG 20 SEQ ID NO: 193 NPD009 (AB  NM_020686S4475/NPD009.r3 GGAGCATTCGAGGTCAAATCA 21 SEQ ID NO: 194 NPD009 (AB NM_020686 S4476/NPD009.p3 TTCCCAGAGTGTCTCACCTCCAGCAGAG 28 SEQ ID NO: 195PR NM_000926 S1336/PR.f6 GCATCAGGCTGTCATTATGG 20 SEQ ID NO: 196 PRNM_000926 S1337/PR.r6 AGTAGTTGTGCTGCCCTTCC 20 SEQ ID NO: 197 PRNM_000926 S4743/PR.p6 TGTCCTTACCTGTGGGAGCTGTAAGGTC 28 SEQ ID NO: 198PRKCD NM_006254 S1738/PRKCD.f2 CTGACACTTGCCGCAGAGAA 20 SEQ ID NO: 199PRKCD NM_006254 S1739/PRKCD.r2 AGGTGGTCCTTGGTCTGGAA 20 SEQ ID NO: 200PRKCD NM_006254 S4923/PRKCD.p2 CCCTTTCTCACCCACCTCATCTGCAC 26SEQ ID NO: 201 PTPD1 NM_007039 S3069/PTPD1.f2 CGCTTGCCTAACTCATACTTTCC 23SEQ ID NO: 202 PTPD1 NM_007039 S3070/PTPD1.r2 CCATTCAGACTGCGCCACTT 20SEQ ID NO: 203 PTPD1 NM_007039 S4822IPTPD1.p2 TCCACGCAGCGTGGCACTG 19SEQ ID NO: 204 RAB6C NM_032144 S5535/RAB6C.f1 GCGACAGCTCCTCTAGTTCCA 21SEQ ID NO: 205 RAB6C NM_032144 S5537/RAB6C.p1 TTCCCGAAGTCTCCGCCCG 19SEQ ID NO: 206 RAB6C NM_032144 S5538/RAB6C.r1 GGAACACCAGCTTGAATTTCCT 22SEQ ID NO: 207 RALBP1 NM_006788 S5853/RALBP1.f1GGTGTCAGATATAAATGTGCAAATGC 26 SEQ ID NO: 208 RALBP1 NM_006788S5854/RALBP1.r1 TTCGATATTGCCAGCAGCTATAAA 24 SEQ ID NO: 209 RALBP1NM_006788 S5855/RALBP1.p1 TGCTGTCCTGTCGGTCTCAGTACGTTCA 28 SEQ ID NO: 210RAP1GDS1  NM_021159 S5306/RAP1GD.f2 TGTGGATGCTGGATTGATTT 20SEQ ID NO: 211 RAP1GDS1  NM_021159 S5307/RAP1GD.r2  AAGCAGCACTTCCTGGTCTT20 SEQ ID NO: 212 RAP1GDS1  NM_021159 S5308/RAP1GD.p2CCACTGGTGCAGCTGCTAAATAGCA 25 SEQ ID NO: 213 RASSF1 NM_007182S2393/RASSF1.f3 AGTGGGAGACACCTGACCTT 20 SEQ ID NO: 214 RASSF1 NM_007182S2394/RASSF1.r3 TGATCTGGGCATTGTACTCC 20 SEQ ID NO: 215 RASSF1 NM_007182S4909/RASSF1.p3 TTGATCTTCTGCTCAATCTCAGCTTGAGA 29 SEQ ID NO: 216 rhoCNM_005167 S2162/rhoC.f1 CCCGTTCGGTCTGAGGAA 18 SEQ ID NO: 217 rhoCNM_005167 S2163/rhoC.r1 GAGCACTCAAGGTAGCCAAAGG 22 SEQ ID NO: 218 rhoCNM_005167 S5042/rhoC.p1 TCCGGTTCGCCATGTCCCG 19 SEQ ID NO: 219 RUNX1NM_001754 S4588/RUNX1.f2 AACAGAGACATTGCCAACCA 20 SEQ ID NO: 220 RUNX1NM_001754 S4589/RUNX1.r2 GTGATTTGCCCAGGAAGTTT 20 SEQ ID NO: 221 RUNX1NM_001754 S4590/RUNX1.p2 TTGGATCTGCTTGCTGTCCAAACC 24 SEQ ID NO: 222SEMA3F NM_004186 S2857/SEMA3F.f3 CGCGAGCCCCTCATTATACA 20 SEQ ID NO: 223SEMA3F NM_004186 S2858/SEMA3F.r3 CACTCGCCGTTGACATCCT 19 SEQ ID NO: 224SEMA3F NM_004186 S4972/SEMA3F.p3 CTCCCCACAGCGCATCGAGGAA 22SEQ ID NO: 225 SGCB NM_000232 S5752/SGCB.f1 CAGTGGAGACCAGTTGGGTAGTG 23SEQ ID NO: 226 SGCB NM_000232 S5753/SGCB.r1 CCTTGAAGAGCGTCCCATCA 20SEQ ID NO: 227 SGCB NM_000232 S5754/SGCB.p1 CACACATGCAGAGCTTGTAGCGTACCCA28 SEQ ID NO: 228 STAT1 NM_007315 S1542/STAT1.f3 GGGCTCAGCTTTCAGAAGTG 20 SEQ ID NO: 229 STAT1 NM_007315 S1543/STAT1.r3 ACATGTTCAGCTGGTCCACA 20SEQ ID NO: 230 STAT1 NM_007315 S4878/STAT1.p3 TGGCAGTTTTCTTCTGTCACCAAAA25 SEQ ID NO: 231 STAT3 NM_003150 S1545/STAT3.f1 TCACATGCCACTTTGGTGTT 20SEQ ID NO: 232 STAT3 NM_003150 S1546/STAT3.r1 CTTGCAGGAAGCGGCTATAC 20SEQ ID NO: 233 STAT3 NM_003150 S4881/STAT3.p1 TCCTGGGAGAGATTGACCAGCA 22SEQ ID NO: 234 TBP NM_003194 S0262/TBP.f1 GCCCGAAACGCCGAATATA 19SEQ ID NO: 235 TBP NM_003194 S0264/TBP.r1 CGTGGCTCTCTTATCCTCATGAT 23SEQ ID NO: 236 TBP NM_003194 S4751/TBP.p1 TACCGCAGCAAACCGCTTGGG 21SEQ ID NO: 237 TK1 NM_003258 S0866/TK1.f2 GCCGGGAAGACCGTAATTGT 20SEQ ID NO: 238 TK1 NM_003258 S0927/TK1.r2 CAGCGGCACCAGGTTCAG 18SEQ ID NO: 239 TK1 NM_003258 S4798/TK1.p2 CAAATGGCTTCCTCTGGAAGGTCCCA 26SEQ ID NO: 240 TP53BP1 NM_005657 S1747/TP53BP.f2 TGCTGTTGCTGAGTCTGTTG 20SEQ ID NO: 241 TP53BP1 NM_O05657 S1748/TP53BP.r2 CTTGCCTGGCTTCACAGATA 20SEQ ID NO: 242 TP53BP1 NM_005657 S4924/TP53BP.p2CCAGTCCCCAGAAGACCATGTCTG 24 SEQ ID NO: 243 TUBB NM_001069 S5826/TUBB.f3TGTGGTGAGGAAGGAGTCAG 20 SEQ ID NO: 244 TUBB NM_001069 S5827/TUBB.r3CCCAGAGAGTGGGTCAGC 18 SEQ ID NO: 245 TUBB NM_001069 S5828/TUBB.p3CTGTGACTGTCTCCAGGGCTTCCA 24 SEQ ID NO: 246 VCAM1 NM_001078S3505/VCAM1.f1 TGGCTTCAGGAGCTGAATACC 21 SEQ ID NO: 247 VCAM1 NM_001078S3506/VCAM1.r1 TGCTGTCGTGATGAGAAAATAGTG 24 SEQ ID NO: 248 VCAM1NM_001078 S3507/VCAM1.p1 CAGGCACACACAGGTGGGACACAAAT 26 SEQ ID NO: 249Wnt-5a NM_003392 S6183/Wnt-5a.f1 GTATCAGGACCACATGCAGTACATC 25SEQ ID NO: 250 Wnt-5a NM_003392 S6184/Wnt-5a.r1  TGTCGGAATTGATACTGGCATT22 SEQ ID NO: 251 Wnt-5a NM_003392 S6185/Wnt-5a.p1TTGATGCCTGTCTTCGCGCCTTCT 24 SEQ ID NO: 252 ZNF38 NM_145914S5593/ZNF38.f3 TTTCCAAACATCAGCGAGTC 20 SEQ ID NO: 253 ZNF38 NM_145914S5594/ZNF38.r3 AACAGGAGCGCTTGAAAGTT 20 SEQ ID NO: 254 ZNF38 NM_145914S5595/ZNF38.p3  ACGGTGCTTCTCCCTCTCCAGTG 23 SEQ ID NO: 255

TABLE 3 Sequence A-Catenin  NM_001903CGTTCCGATCCTCTATACTGCATCCCAGGCATGCCTACA SEQ ID NO: 256GCACCCTGATGTCGCAGCCTATAAGGCCAACAGGGACCT ABCC5 NM_005688TGCAGACTGTACCATGCTGACCATTGCCCATCGCCTGCA SEQ ID NO: 257CACGGTTCTAGGCTCCGATAGGATTATGGTGCTGGCC AK055699 AK055699CTGCATGTGATTGAATAAGAAACAAGAAAGTGACCACAC SEQ ID NO: 258CAAAGCCTCCCTGGCTGGTGTACAGGGATCAGGTCCACA AKAP-2 NM_007203ACGAATTGTCGGTGAGGTCTCAGGATACCACAGTCCTGG SEQ ID NO: 259AGACCCTATCCAATGATTTCAGCATGGAC AKT1 NM_005163CGCTTCTATGGCGCTGAGATTGTGTCAGCCCTGGACTAC SEQ ID NO: 260CTGCACTCGGAGAAGAACGTGGTGTACCGGGA AKT2 NM_001626TCCTGCCACCCTTCAAACCTCAGGTCACGTCCGAGGTCG SEQ ID NO: 261ACACAAGGTACTTCGATGATGAATTTACCGCC APC NM_000038GGACAGCAGGAATGTGTTTCTCCATACAGGTCACGGGGA SEQ ID NO: 262GCCAATGGTTCAGAAACAAATCGAGTGGGT BAD NM_032989GGGTCAGGTGCCTCGAGATCGGGCTTGGGCCCAGAGCAT SEQ ID NO: 263GTTCCAGATCCCAGAGTTTGAGCCGAGTGAGCAG BAG1 NM_004323CGTTGTCAGCACTTGGAATACAAGATGGTTGCCGGGTCATG SEQ ID NO: 264TTAATTGGGAAAAAGAACAGTCCACAGGAAGAGGTTGAAC BBC3 NM_014417CCTGGAGGGTCCTGTACAATCTCATCATGGGACTCCTGCCCT SEQ ID NO: 265TACCCAGGGGCCACAGAGCCCCCGAGATGGAGCCCAATTAG Bcl2  NM_000633CAGATGGACCTAGTACCCACTGAGATTTCCACGCCGAA SEQ ID NO: 266GGACAGCGATGGGAAAAATGCCCTTAAATCATAGG CCND1 NM_001758GCATGTTCGTGGCCTCTAAGATGAAGGAGACCATCCCCCTG SEQ ID NO: 267ACGGCCGAGAAGCTGTGCATCTACACCG CD18 NM_000211CGTCAGGACCCACCATGTCTGCCCCATCACGCGGCCGAGAC SEQ ID NO: 268ATGGCTTGGCCACAGCTCTTGAGGATGTCACCAATTAACC CD31 NM_000442TGTATTTCAAGACCTCTGTGCACTTATTTATGAACCTG SEQ ID NO: 269CCCTGCTCCCACAGAACACAGCAATTCCTCAGGCTAA CD3z NM_000734AGATGAAGTGGAAGGCGCTTTTCACCGCGGCCATCCTGCAG SEQ ID NO: 270GCACAGTTGCCGATTACAGAGGCA CD9 NM_001769GGGCGTGGAACAGTTTATCTCAGACATCTGCCCCAAGA SEQ ID NO: 271AGGACGTACTCGAAACCTTCACCGTG CDC20 NM_001255TGGATTGGAGTTCTGGGAATGTACTGGCCGTGGCACTGGAC SEQ ID NO: 272AACAGTGTGTACCTGTGGAGTGCAAGC CDC25B NM_021874AAACGAGCAGTTTGCCATCAGACGCTTCCAGTCTATGCCGGTG SEQ ID NO: 273AGGCTGCTGGGCCACAGCCCCGTGCTTCGGAACATCACCAAC CEGP1 NM_020974TGACAATCAGCACACCTGCATTCACCGCTCGGAAGAGGGCCT SEQ ID NO: 274GAGCTGCATGAATAAGGATCACGGCTGTAGTCACA CGA  NM_001275CTGAAGGAGCTCCAAGACCTCGCTCTCCAAGGCGCCAAGGAG SEQ ID NO: 275(CHGA  official) AGGGCACATCAGCAGAAGAAACACAGCGGTTTTG COL1A1 NM_000088GTGGCCATCCAGCTGACCTTCCTGCGCCTGATGTCCACCGAG SEQ ID NO: 276GCCTCCCAGAACATCACCTACCACTG CoL1A2 NM_000089CAGCCAAGAACTGGTATAGGAGCTCCAAGGACAAGAAACACG SEQ ID NO: 277TCTGGCTAGGAGAAACTATCAATGCTGGCAGCCAGTTT Contig  XM_058945CGACAGTTGCGATGAAAGTTCTAATCTCTTCCCTCCTCCTGT SEQ ID NO: 278 51037TGCTGCCACTAATGCTGATGTCCATGGTCTCTAGCAGCC CRABP1 NM_004378AACTTCAAGGTCGGAGAAGGCTTTGAGGAGGAGACCGTGGAC SEQ ID NO: 279GGACGCAAGTGCAGGAGTTTAGCCA CRIP2 NM_001312GTGCTACGCCACCCTGTTCGGACCCAAAGGCGTGAACATCGG SEQ ID NO: 280GGGCGCGGGCTCCTACATCTACGAGAAGCCCCTG CYBA NM_000101GGTGCCTACTCCATTGTGGCGGGCGTGTTTGTGTGCCTGCTG SEQ ID NO: 281GAGTACCCCCGGGGGAAGAGGAAGAAGGGCTCCAC DHPS NM_013407GGGAGAACGGGATCAATAGGATCGGAAACCTGCTGGTGCCCA SEQ ID NO: 282ATGAGAATTACTGCAAGTTTGAGGACTGGCTGATGC DICER1 NM_177438TCCAATTCCAGCATCACTTTGGAGAAAAGCTGTTTTGTCT SEQ ID NO: 283CCCCAGCATACTTTATCGCCTTCACTGCC DR4 NM_003844TGCACAGAGGGTGTGGGTTACACCAATGCTTCCAACAATTTG SEQ ID NO: 284TTTGCTTGCCTCCCATGTACAGCTTGTAAATCAGATGAAGA E2F1 NM_005225ACTCCCTCTACCCTTGAGCAAGGGCAGGGGTCCCTGAGCTGT SEQ ID NO: 285TCTTCTGCCCCATACTGAAGGAACTGAGGCCTG ER2 NM_001437TGGTCCATCGCCAGTTATCACATCTGTATGCGGAACCTCAAA SEQ ID NO: 286AGAGTCCCTGGTGTGAAGCAAGATCGCTAGAACA ErbB3 NM_001982CGGTTATGTCATGCCAGATACACACCTCAAAGGTACTCCCTC SEQ ID NO: 287CTCCCGGGAAGGCACCCTTTCTTCAGTGGGTCTCAGTTC ERBB4 NM_005235TGGCTCTTAATCAGTTTCGTTACCTGCCTCTGGAGAATTTACG SEQ ID NO: 288CATTATTCGTGGGACAAAACTTTATGAGGATCGATATGCCTTG ERCC1 NM_001983GTCCAGGTGGATGTGAAAGATCCCCAGCAGGCCCTCAAGGAG SEQ ID NO: 289CTGGCTAAGATGTGTATCCTGGCCG ERK1 Z11696ACGGATCACAGTGGAGGAAGCGCTGGCTCACCCCTACCTGGA SEQ ID NO: 290GCAGTACTATGACCCGACGGATGAG ESRRG NM_001438CCAGCACCATTGTTGAAGATCCCCAGACCAAGTGTGAATACA SEQ ID NO: 291TGCTCAACTCGATGCCCAAGAGACT faSl NM_000639GCACTTTGGGATTCTTTCCATTATGATTCTTTGTTACAGGCACC SEQ ID NO: 292GAGAATGTTGTATTCAGTGAGGGTCTTCTTACATGC FBXO5 NM_012177GGCTATTCCTCATTTTCTCTACAAAGTGGCCTCAGTGAACATGAA SEQ ID NO: 293GAAGGTAGCCTCCTGGAGGAGAATTTCGGTGACAGTCTACAATCC FHIT NM_002012CCAGTGGAGCGCTTCCATGACCTGCGTCCTGATGAAGTGGCC SEQ ID NO: 294GATTTGTTTCAGACGACCCAGAGAG FUS NM_004960GGATAATTCAGACAACAACACCATCTTTGTGCAAGGCCTG SEQ ID NO: 295GGTGAGAATGTTACAATTGAGTCTGTGGCTGATTACTTCA FYN NM_002037GAAGCGCAGATCATGAAGAAGCTGAAGCACGACAAGCTGGTCCAG SEQ ID NO: 296CTCTATGCAGTGGTGTCTGAGGAG G-Catenin NM_002230TCAGCAGCAAGGGCATCATGGAGGAGGATGAGGCCTGCGGGCGCC SEQ ID NO: 297AGTACACGCTCAAGAAAACCACC GATA3 NM_002051CAAAGGAGCTCAdGIGGTGICTGTGTTCCAACCACTGAATCTGGA SEQ ID NO: 298CCCCATCTGTGAATAAGCCATTCTGACTC GBP1 NM_002053TTGGGAAATATTTGGGCATTGGTCTGGCCAAGTCTACAATGTCC SEQ ID NO: 299CAATATCAAGGACAACCACCCTAGCTTCT GBP2 NM_004120GCATGGGAACCATCAACCAGCAGGCCATGGACCAACTTCACTATG SEQ ID NO: 300TGACAGAGCTGACAGATCGAATCAAGGCAAACTCCTCA GGPS1 NM_004837CTCCGACGTGGCTTTCCAGTGGCCCACAGCATCTATGGAATCCCA SEQ ID NO: 301TCTGTCATCAATTCTGCCAATTACG GRB7 NM_005310CCATCTGCATCCATCTTGTTTGGGCTCCCCACCCTTGAGAAGTGC SEQ ID NO: 302CTCAGATAATACCCTGGTGGCC Hepsin NM_002151AGGCTGCTGGAGGTCATCTCCGTGTGTGATTGCCCCAGAGGCCGT SEQ ID NO: 303TTCTTGGCCGCCATCTGCCAAGACTGTGGCCGCAGGAAG HLA-DPB1 NM_002121TCCATGATGGTTCTGCAGGTTTCTGCGGCCCCCCGGACAGTGGCT SEQ ID NO: 304CTGACGGCGTTACTGATGGTGCTGCTCA ID2 NM_002166AACGACTGCTACTCCAAGCTCAAGGAGCTGGTGCCCAGCATCCCC SEQ ID NO: 305CAGAACAAGAAGGTGAGCAAGATGGAAATCC IGF1R NM_000875GCATGGTAGCCGAAGATTTCACAGTCAAAATCGGAGATTTTGGTA SEQ ID NO: 306TGACGCGAGATATCTATGAGACAGACTATTACCGGAAA IL6 NM_000600CCTGAACCTTCCAAAGATGGCTGAAAAAGATGGATGCTTCCAATC SEQ ID NO: 307TGGATTCAATGAGGAGACTTGCCTGGT ILT-2 NM_006669AGCCATCACTCTCAGTGCAGCCAGGTCCTATCGTGGCCCCTGAGG SEQ ID NO: 308AGACCCTGACTCTGCAGT IRS1 NM_005544CCACAGCTCACCTTCTGTCAGGTGTCCATCCCAGCTCCAGCCAGC SEQ ID NO: 309TCCCAGAGAGGAAGAGACTGGCACTGAGG KRT18 NM_000224AGAGATCGAGGCTCTCAAGGAGGAGCTGCTCTTCATGAAGAAGAA SEQ ID NO: 310CCACGAAGAGGAAGTAAAAGGCC MAPK14 NM_139012TGAGTGGAAAAGCCTGACCTATGATGAAGTCATCAGCTTTGTGCC SEQ ID NO: 311ACCACCCCTTGACCAAGAAGAGATGGAGTCC MCM2 NM_004526GACTTTTGCCCGCTACCTTTCATTCCGGCGTGACAACAATGAGCT SEQ ID NO: 312GTTGCTCTTCATACTGAAGCAGTTAGTGGC MCM6 NM_005915TGATGGTCCTATGTGTCACATTCATCACAGGTTTCATACCAACAC SEQ ID NO: 313AGGCTTCAGCACTTCCTTTGGTGTGTTTCCTGTCCCA MCP1 NM_002982CGCTCAGCCAGATGCAATCAATGCCCCAGTCACCTGCTGTTATAA SEQ ID NO: 314CTTCACCAATAGGAAGATCTCAGTGC MGMT NM_002412GTGAAATGAAACGCACCACACTGGACAGCCCTTTGGGGAAGCTGG SEQ ID NO: 315AGCTGTCTGGTTGTGAGCAGGGTC MMP12 NM_002426CCAACGCTTGCCAAATCCTGACAATTCAGAACCAGCTCTCTGTGA SEQ ID NO: 316CCCCAATTTGAGTTTTGATGCTGTCACTACCGT MSH3 NM_002439TGATTACCATCATGGCTCAGATTGGCTCCTATGTTCCTGCAGAAG SEQ ID NO: 317AAGCGACAATTGGGATTGTGGATGGCATTTTCACAAG MTA1 NM_004689CCGCCCTCACCTGAAGAGAAACGCGCTCCTTGGCGGACACTGGGG SEQ ID NO: 318GAGGAGAGGAAGAAGCGCGGCTAACTTATTCC MUC1 NM_002456GGCCAGGATCTGTGGTGGTACAATTGACTCTGGCCTTCCGAGAAG SEQ ID NO: 319GTACCATCAATGTCCACGACGTGGAG NPD009   NM_020686GGCTGTGGCTGAGGCTGTAGCATCTCTGCTGGAGGTGAGACACTC SEQ ID NO: 320(ABAT officia TGGGAACTGATTTGACCTCGAATGCTCC PR NM_000926GCATCAGGCTGTCATTATGGTGTCCTTACCTGTGGGAGCTGTAAG SEQ ID NO: 321GTCTTCTTTAAGAGGGCAATGGAAGGGCAGCACAACTACT PRKCD NM_006254CTGACACTTGCCGCAGAGAATCCCTTTCTCACCCACCTCATCTGC SEQ ID NO: 322ACCTTCCAGACCAAGGACCACCT PTPD1 NM_007039CGCTTGCCTAACTCATACTTTCCCGTTGACACTTGATCCACGCAG SEQ ID NO: 323CGTGGCACTGGGACGTAAGTGGCGCAGTCTGAATGG RAB6C NM_032144GCGACAGCTCCTCTAGTTCCACCATGTCCGCGGGCGGAGACTTCG SEQ ID NO: 324GGAATCCGCTGAGGAAATTCAAGCTGGTGTTCC RALBP1 NM_006788GGTGTCAGATATAAATGTGCAAATGCCTTCTTGCTGTCCTGTCGG SEQ ID NO: 325TCTCAGTACGTTCACTTTATAGCTGCTGGCAATATCGAA RAP1GDS1 NM_021159TGTGGATGCTGGATTGATTTCACCACTGGTGCAGCTGCTAAATAG SEQ ID NO: 326CAAAGACCAGGAAGTGCTGCTT RASSF1 NM_007182AGTGGGAGACACCTGACCTTTCTCAAGCTGAGATTGAGCAGAAGA SEQ ID NO: 327TCAAGGAGTACAATGCCCAGATCA rhoC NM_005167CCCGTTCGGTCTGAGGAAGGCCGGGACATGGCGAACCGGATCAGT SEQ ID NO: 328GCCTTTGGCTACCTTGAGTGCTC RUNX1 NM_001754AACAGAGACATTGCCAACCATATTGGATCTGCTTGCTGTCCAAAC SEQ ID NO: 329CAGCAAACTTCCTGGGCAAATCAC SEMA3F NM_004186CGCGAGCCCCTCATTATACACTGGGCAGCCTCCCCACAGCGCATC SEQ ID NO: 330GAGGAATGCGTGCTCTCAGGCAAGGATGTCAACGGCGAGTG SGCB NM_000232CAGTGGAGACCAGTTGGGTAGTGGTGACTGGGTACGCTACAAGCT SEQ ID NO: 331CTGCATGTGTGCTGATGGGACGCTCTTCAAGG STAT1 NM_007315GGGCTCAGCTTTCAGAAGTGCTGAGTTGGCAGTTTTCTTCTGTCA SEQ ID NO: 332CCAAAAGAGGTCTCAATGTGGACCAGCTGAACATGT STAT3 NM_003150TCACATGCCACTTTGGTGTTTCATAATCTCCTGGGAGAGATTGAC SEQ ID NO: 333CAGCAGTATAGCCGCTTCCTGCAAG TBP NM_003194GCCCGAAACGCCGAATATAATCCCAAGCGGTTTGCTGCGGTAATC SEQ ID NO: 334ATGAGGATAAGAGAGCCACG TK1 NM_003258GCCGGGAAGACCGTAATTGTGGCTGCACTGGATGGGACCTTCCAG SEQ ID NO: 335AGGAAGCCATTTGGGGCCATCCTGAACCTGGTGCCGCTG TP53BP1 NM_005657TGCTGTTGCTGAGTCTGTTGCCAGTCCCCAGAAGACCATGTCTGT SEQ ID NO: 336GTTGAGCTGTATCTGTGAAGCCAGGCAAG TUBB NM_001069TGTGGTGAGGAAGGAGTCAGAGAGCTGTGACTGTCTCCAGGGCTT SEQ ID NO: 337CCAGCTGACCCACTCTCTGGG VCAM1 NM_001078TGGCTTCAGGAGCTGAATACCCTCCCAGGCACACACAGGTGGGAC SEQ ID NO: 338ACAAATAAGGGTTTTGGAACCACTATTTTCTCATCACGACAGCA Wnt-5a NM_003392GTATCAGGACCACATGCAGTACATCGGAGAAGGCGCGAAGACAGG SEQ ID NO: 339CATCAAAGAATGCCAGTATCAATTCCGACA ZNF38 NM_145914TTTCCAAACATCAGCGAGTCCACACTGGAGAGGGAGAAGCACCGT SEQ ID NO: 340AACTTTCAAGCGCTCCTGTT

1. A method for predicting the likelihood of pathologic completeresponse to chemotherapy that comprises administration of bothanthracycline and taxane in a human patient diagnosed with breast cancercomprising: (a) assaying the level of an CEGP1 RNA or protein in atissue sample obtained from a breast tumor of said human patient; (b)normalizing said level against a level of at least one reference RNA orprotein in said tissue sample to provide a normalized CEGP1 expressionlevel; and (c) predicting the likelihood of pathologic complete responseof the human patient to chemotherapy that comprises administration ofanthracycline and taxane, wherein said prediction is based on saidnormalized CEGP1 expression level, wherein CEGP1 expression isnegatively correlated with the likelihood of pathologic completeresponse to chemotherapy comprising administration of anthracycline andtaxane.
 2. The method of claim 1, wherein said breast tumor is aninvasive breast tumor.
 3. The method of claim 2, wherein said breasttumor is a stage II or stage III breast tumor.
 4. The method of claim 1,wherein said chemotherapy is adjuvant chemotherapy.
 5. The method ofclaim 1, wherein said chemotherapy is neoadjuvant chemotherapy.
 6. Themethod of claim 1, wherein said taxane is docetaxel or paclitaxel. 7.The method of claim 6, wherein said taxane is docetaxel.
 8. The methodof claim 1, wherein said chemotherapy further comprises theadministration of an additional anti-cancer agent.
 9. The method ofclaim 1, wherein said anthracycline is doxorubicin.
 10. The method ofclaim 8, wherein said additional anti-cancer agent is a topoisomeraseinhibitor.
 11. The method of claim 1, wherein said tissue sample isfixed, paraffin-embedded, fresh, or frozen.
 12. The method of claim 1,wherein the tissue sample is a needle or core biopsy.
 13. The method ofclaim 1, wherein the tissue sample is obtained by fine needleaspiration.
 14. The method of claim 1, wherein said assaying is done byreverse transcriptase polymerase chain reaction (RT-PCR) or anotherPCR-based method.
 15. The method of claim 1, wherein said assaying isdone by immunohistochemistry.
 16. The method of claim 1, furthercomprising assaying the level of an RNA transcript or protein product ofone or more of the following genes: TBP, ILT.2, ABCC5, CD18, GATA3,DICER1, MSH3, GBP1, IRS1, CD3z, fasl, TUBB, BAD, ERCC1, MCM6, PR, APC,GGPS1, KRT18, ESRRG, E2F1, AKT2, A.Catenin, NPD009, MAPK14, RUNX1, ID2,G.Catenin, FBXO5, FHIT, MTA1, ERBB4, FUS, BBC3, IGF1R, CD9, TP53BP1,MUC1, IGFBP5, rhoC, RALBP1, CDC20, STAT3, ERK1, HLA.DPB1, SGCB, CGA,DHPS, MGMT, CRIP2, MMP12, ErbB3, RAP1GDS1, CDC25B, IL6, CCND1, CYBA,PRKCD, DR4, Hepsin, CRABP1, AK055699, Contig.51037, VCAM1, FYN, GRB7,AKAP.2, RASSF1, MCP1, ZNF38, MCM2, GBP2, SEMA3F, CD31, COL1A1, ER2,BAG1, AKT1, COL1A2, STAT1, Wnt.5a, PTPD1, RAB6C, TK1, ErbB2, CCNB1,BIRC5, STK6, MKI67, MYBL2, MMP11, CTSL2, CD68, GSTM1, BCL2, and ESR1.17. The method of claim 16, further comprising normalizing said level ofan RNA transcript of said one or more genes, or a protein productthereof, against a level of at least one reference RNA transcript or aprotein product thereof in said tissue sample to provide a normalizedexpression level of said one or more genes, wherein (a) the normalizedexpression level of one or more of ILT.2, CD18, GBP1, CD3z, fasl, MCM6,E2F1, ID2, FBXO5, CDC20, HLA.DPB1, CGA, MMP12, CDC25B, IL6, CYBA, DR4,CRABP1, Contig.51037, VCAM1, FYN, GRB7, AKAP.2, RASSF1, MCP1, MCM2,GBP2, CD31, ER2, STAT1, TK1, ERBB2, CCNB1, BIRC5, STK6, MKI67, MYBL2,MMP11, CD68 and CTSL2 positively correlates with the likelihood of apathologic complete response; and (b) the normalized expression level ofone or more of TBP, ABCC5, GATA3, DICER1, MSH3, IRS1, TUBB, BAD, ERCC1,PR, APC, GGPS1, KRT18, ESRRG, AKT2, A.Catenin, NPD009, MAPK14, RUNX1,G.Catenin, FHIT, MTA1, ErbB4, FUS, BBC3, IGF1R, CD9, TP53BP1, MUC1,IGFBP5, rhoC, RALBP1, STAT3, ERK1, SGCB, DHPS, MGMT, CRIP2, ErbB3,RAP1GDS1, CCND1, PRKCD, Hepsin, AK055699, ZNF38, SEMA3F, COL1A1, BAG1,AKT1, COL1A2, Wnt.5a, PTPD1, RAB6C, GSTM1, BCL2 and ESR1, negativelycorrelates with the likelihood of a pathologic complete response. 18.The method of claim 8, wherein said additional anti-cancer agent is acyclophosphamide.
 19. The method of claim 1, wherein said assayingcomprises determining the level of an RNA transcript of CEGP1.
 20. Themethod of claim 1, wherein said assaying comprises determining the levelof CEGP1 protein.
 21. The method of claim 1, further comprising creatinga report indicating the likelihood of pathologic complete response ofthe patient to said chemotherapy.
 22. The method of claim 1, furthercomprising calculating a quantitative score indicating the likelihood ofa pathologic complete response to chemotherapy that comprisesadministration of both anthracycline and taxane, wherein saidquantitative score is calculated using said normalized CEGP1 expressionlevel and the negative correlation between the normalized CEGP1 leveland the likelihood of a pathological complete response to chemotherapythat comprises administration of both anthracycline and taxane.